In this paper we detail the methods used for obstacle avoidance, path planning, and trajectory tracking that helped us win the adult-sized, autonomous humanoid soccer league in RoboCup 2024. Our team was undefeated for all seated matches and scored 45 goals over 6 games, winning the championship game 6 to 1. During the competition, a major challenge for collision avoidance was the measurement noise coming from bipedal locomotion and a limited field of view (FOV). Furthermore, obstacles would sporadically jump in and out of our planned trajectory. At times our estimator would place our robot inside a hard constraint. Any planner in this competition must also be be computationally efficient enough to re-plan and react in real time. This motivated our approach to trajectory generation and tracking. In many scenarios long-term and short-term planning is needed. To efficiently find a long-term general path that avoids all obstacles we developed DAVG (Dynamic Augmented Visibility Graphs). DAVG focuses on essential path planning by setting certain regions to be active based on obstacles and the desired goal pose. By augmenting the states in the graph, turning angles are considered, which is crucial for a large soccer playing robot as turning may be more costly. A trajectory is formed by linearly interpolating between discrete points generated by DAVG. A modified version of model predictive control (MPC) is used to then track this trajectory called cf-MPC (Collision-Free MPC). This ensures short-term planning. Without having to switch formulations cf-MPC takes into account the robot dynamics and collision free constraints. Without a hard switch the control input can smoothly transition in cases where the noise places our robot inside a constraint boundary. The nonlinear formulation runs at approximately 120 Hz, while the quadratic version achieves around 400 Hz.

In limbed robotics, end-effectors must serve dual functions, such as both feet for locomotion and grippers for grasping, which presents design challenges. This paper introduces a multi-modal end-effector capable of transitioning between flat and line foot configurations while providing grasping capabilities. MAGPIE integrates eight-axis force sensing using proposed mechanisms with Hall effect sensors, enabling both contact and tactile force measurements. We present a computational design framework for our sensing mechanism that accounts for noise and interference, allowing for desired sensitivity and force ranges and generating ideal inverse models. The hardware implementation of MAGPIE is validated through experiments, demonstrating its capability as a foot and verifying the performance of the sensing mechanisms, ideal models, and gated network-based models.

This paper presents a novel approach through the design and implementation of Cycloidal Quasi-Direct Drive actuators for legged robotics. The cycloidal gear mechanism, with its inherent high torque density and mechanical robustness, offers significant advantages over conventional designs. By integrating cycloidal gears into the Quasi-Direct Drive framework, we aim to enhance the performance of legged robots, particularly in tasks demanding high torque and dynamic loads, while still keeping them lightweight. Additionally, we develop a torque estimation framework for the actuator using an Actuator Network, which effectively reduces the sim-toreal gap introduced by the cycloidal drive's complex dynamics. This integration is crucial for capturing the complex dynamics of a cycloidal drive, which contributes to improved learning efficiency, agility, and adaptability for reinforcement learning.

State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state of the robot’s trunk. Leveraging joint encoder and IMU measurements, our Kalman filter is enhanced through a single-rigid body model that incorporates ground reaction force control outputs from convex Model Predictive Control optimization. The estimation is further refined through Gated Recurrent Units, which also considers semantic insights and robot height from a Vision Transformer autoencoder applied on depth images. This framework not only furnishes accurate robot state estimates, including uncertainty evaluations, but can minimize the nonlinear errors that arise from sensor measurements and model simplifications through learning. The proposed methodology is evaluated in hardware using a quadruped robot on various terrains, yielding a 65% improvement on the Root Mean Squared Error compared to our VIO SLAM baseline. Code example: https://github.com/AlexS28/OptiState

Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world. However, DR often requires careful tuning of randomization parameters. Methods like Bayesian Domain Randomization (Bayesian DR) and Active Domain Randomization (Adaptive DR) address this issue by automating parameter range selection using real-world experience. While effective, these algorithms often require long computation time, as a new policy is trained from scratch every iteration. In this work, we propose Adaptive Bayesian Domain Randomization via Strategic Fine-tuning (BayRnTune), which inherits the spirit of BayRn but aims to significantly accelerate the learning processes by fine-tuning from previously learned policy. This idea leads to a critical question: which previous policy should we use as a prior during fine-tuning? We investigated four different fine-tuning strategies and compared them against baseline algorithms in five simulated environments, ranging from simple benchmark tasks to more complex legged robot environments. Our analysis demonstrates that our method yields better rewards in the same amount of timesteps compared to vanilla domain randomization or Bayesian DR.

Humans can effortlessly perform very complex, dexterous manipulation tasks by reacting to sensor observations. In contrast, robots can not perform reactive manipulation and they mostly operate in open-loop while interacting with their environment. Consequently, the current manipulation algorithms either are inefficient in performance or can only work in highly structured environments. In this paper, we present closed-loop control of a complex manipulation task where a robot uses a tool to interact with objects. Manipulation using a tool leads to complex kinematics and contact constraints that need to be satisfied for generating feasible manipulation trajectories. We first present an open-loop controller design using Non-Linear Programming (NLP) that satisfies these constraints. In order to design a closed-loop controller, we present a pose estimator of objects and tools using tactile sensors. Using our tactile estimator, we design a closed-loop controller based on Model Predictive Control (MPC). The proposed algorithm is verified using a 6 DoF manipulator on tasks using a variety of objects and tools. We verify that our closed-loop controller can successfully perform tool manipulation under several unexpected contacts.

The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments involving humans, unlike many heavy and rigid robots. However, their unique and sensitive dynamics impose challenges to obtaining robust control policies in the real world. In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification and our novel residual physics learning method, Environment Mimic (EnvMimic). First, we model the nonlinear dynamics of the actuators by collecting hardware data and optimizing the simulation parameters. Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy to match real-world trajectories, which enables us to model residual physics with greater fidelity. We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones. We finally demonstrate that the improved simulator allows us to learn better walking and turning policies that can be successfully deployed on the hardware of BALLU.

Generating dynamic jumping motions on legged robots remains a challenging control problem as the full flight phase and large landing impact are expected. Compared to quadrupedal robots or other multi-legged robots, bipedal robots place higher requirements for the control strategy given a much smaller support polygon. To solve this problem, a novel heuristic landing planner is proposed in this paper. With the momentum feedback during the flight phase, landing locations can be updated to minimize the influence of uncertainties from tracking errors or external disturbances when landing. To the best of our knowledge, this is the first approach to take advantage of the flight phase to reduce the impact of the jump landing which is implemented in the actual robot. By integrating it with a modified kino-dynamics motion planner with centroidal momentum and a low-level controller which explores the whole-body dynamics to hierarchically handle multiple tasks, a complete and versatile jumping control framework is designed in this paper. Extensive results of simulation and hardware jumping experiments on a miniature bipedal robot with proprioceptive actuation are provided to demonstrate that the proposed framework is able to achieve human-like efficient and robust jumping tasks, including directional jump, twisting jump, step jump, and somersaults.

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines learning methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. Gathering sufficient training data to employ these methods still present a challenge since getting data from traditional solvers are slow and newer learning approaches still require large amounts of data. In order to scale up and make these hybrid learning approaches more manageable we propose ReDUCE, a method that exploits structure within small to medium size datasets. We also introduce the bookshelf organization problem as an MINLP as a way to measure performance of solvers with ReDUCE. Results show that existing algorithms with ReDUCE can solve this problem within a few seconds, a significant improvement over the original formulation. ReDUCE is demonstrated as a high level planner for a robotic arm for the bookshelf problem.

As the study of humanoid robots becomes a world-wide interdisciplinary research field, the demand for a cost-effective bipedal robot system capable of dynamic behaviors is growing exponentially. This paper presents a miniature bipedal robot named Bipedal Robot Unit with Compliance Enhanced (BRUCE). Each leg of BRUCE has five degrees of freedom (DoFs), which includes a spherical hip joint, a knee joint, and an ankle joint. To lower the leg inertia, a cable-driven differential pulley system and a linkage mechanism are applied to the hip and ankle joints, respectively. With the proposed design, BRUCE is able to achieve a similar range of motion to a human's lower body. The proprioceptive actuation and contact sensing further prepare BRUCE for interactions with unstructured environments. For real-time control of dynamic motions, a convex formulation for model hierarchy predictive control (MHPC) is introduced. MHPC plans with whole-body dynamics in the near horizon and simplified dynamics in the long horizon to benefit from both model accuracy and computational efficiency. A series of experiments were conducted to evaluate the overall system performance including hip joint analysis, walking, push recovery, and vertical jumping.

This paper presents a convex model predictive control framework for versatile dynamic legged motions with negligible leg dynamics. The framework utilizes the single rigid body model linearly approximated around the operating point. With ground reaction forces as direct control inputs to the system, no reference control trajectory needs to be specified in advance. By using the rotation matrix for the evolution of rotational dynamics, issues arising from other representations can be avoided. Moreover, the rotation matrix is parametrized using the history of angular velocity without introducing additional variables. The effect is that we can still take the orientation into consideration efficaciously without directly working on it. The framework tackles the robot reference tracking problem via trajectory optimization, which is formulated into a standard quadratic program and can be solved efficiently in real time with guaranteed optimality. It was verified on various legged robots with different numbers of legs for performing different types of dynamic motions in the simulation environment. We thus envision a promising future of the proposed convex model predictive control framework in legged robots and potentially in other applications as well.

In this paper we present a motion planner for LIMMS, a modular multi-agent, multi-modal package delivery platform. A single LIMMS unit is a robot that can operate as an arm or leg depending on how and what it is attached to, e.g., a manipulator when it is anchored to walls within a delivery vehicle or a quadruped robot when 4 are attached to a box. Coordinating amongst multiple LIMMS, when each one can take on vastly different roles, can quickly become complex. For such a planning problem we first compose the necessary logic and constraints. The formulation is then solved for skill exploration and can be implemented on hardware after refinement. To solve this optimization problem we use alternating direction method of multipliers (ADMM). The proposed planner is experimented under various scenarios which shows the capability of LIMMS to enter into different modes or combinations of them to achieve their goal of moving shipping boxes.

While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), and contact (e.g., gait) problems. To accelerate the planning process, we propose distributed optimization frameworks based on Alternating Direction Methods of Multipliers (ADMM) to solve the original large-scale Mixed-Integer NonLinear Programming (MINLP). The resulting frameworks use Mixed-Integer Quadratic Programming (MIQP) to solve contact and NonLinear Programming (NLP) to solve nonlinear dynamics, which are more computationally tractable and less sensitive to parameters. Also, we explicitly enforce patch contact constraints from limit surfaces with micro-spine grippers. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45° with a much shorter planning time.

This paper introduces SCALER, a quadrupedal robot that demonstrates climbing on bouldering walls, over-hangs, ceilings and trotting on the ground. SCALER is one of the first high-degrees of freedom four-limbed robots that can free-climb under the Earth's gravity and one of the most mechanically efficient quadrupeds on the ground. Where other state-of-the-art climbers specialize in climbing, SCALER promises practical free-climbing with payload and ground locomotion, which realizes true versatile mobility. A new climbing gait, SKATE gait, increases the payload by utilizing the SCALER body linkage mechanism. SCALER achieves a maximum normalized locomotion speed of 1.87 /s, or 0.56 m/s on the ground and 1.0 /min, or 0.35 m/min in bouldering wall climbing. Payload capacity reaches 233 % of the SCALER weight on the ground and 35 % on the vertical wall. Our GOAT gripper, a mechanically adaptable underactuated two-finger gripper, successfully grasps convex and non-convex objects and supports SCALER.

Dynamic locomotion for legged robots is difficult because the system dynamics are highly nonlinear and complex, nominally underactuated and unstable, multi-input and multi-output, as well as time-variant and hybrid. One usually faces the choice between the intricate full-body dynamics which remains computationally expensive and sometimes even intractable, and the empirically simplified model which inevitably limits the locomotion capability. In this paper, we explore the legged robot dynamics from a different perspective. By decomposing the robot into the body and the legs, with interaction forces and moments connecting them, we enjoy a novel method called Dynamic Model Decomposition that involves lower-dimensional dynamics for each subsystem while their composition maintaining the equivalence to the original full-order robot model. Based on that, we further propose a corresponding model predictive control framework via quadratic programming, which con-siders linearly approximated body dynamics with constrained leg reaction forces as inputs. The overall methodology was successfully applied to a planar five-link biped robot. The simulation results show that the robot is capable of body reference tracking, push recovery, velocity tracking, and even blind locomotion on fairly rough terrain. This suggests a promising dynamic motion control scheme in the future.

For multi-limbed robots, motion planning with posture and force constraints tends to be a difficult optimization problem due to nonlinearities, which also present extended solve times. We propose a multi-stage optimization framework with data-driven inter-stage coupling constraints to address the nonlinearity. Both clustering and evolutionary approaches to find the McCormick envelope relaxations are used to find the problem-specific parameters. The learned constraints are then used in the prior stages, which provides advanced knowledge of the following stages. This leads to improved solve times and interpretability of the results. The planner is validated through multiple walking and climbing tasks on a 10 kg hexapod robot.

Although Trajectory Optimization (TO) is one of the most powerful motion planning tools, it suffers from expensive computational complexity as a time horizon increases in cluttered environments. It can also fail to converge to a globally optimal solution. In this paper, we present Lazy Trajectory Optimization (LTO) that unifies local short-horizon TO and global Graph-Search Planning (GSP) to generate a long-horizon global optimal trajectory. LTO solves TO with the same constraints as the original long-horizon TO with improved time complexity. We also propose a TO-aware cost function that can balance both solution cost and planning time. Since LTO solves many nearly identical TO in a roadmap, it can provide an informed warm-start for TO to accelerate the planning process. We also present proofs of the computational complexity and optimality of LTO. Finally, we demonstrate LTO’s performance on motion planning problems for a 2 DOF free-flying robot and a 21 DOF legged robot, showing that LTO outperforms existing algorithms in terms of its runtime and reliability.

In order to achieve autonomous vertical wall climbing, the transition phase from the ground to the wall requires extra consideration inevitably. This paper focuses on the contact sequence planner to transition between flat terrain and vertical surfaces for multi-limbed climbing robots. To overcome the transition phase, it requires planning both multicontact and contact wrenches simultaneously which makes it difficult. Instead of using a predetermined contact sequence, we consider various motions on different environment setups via modeling contact constraints and limb switchability as complementarity conditions. Two safety factors for toe sliding and motor over-torque are the main tuning parameters for different contact sequences. By solving as a nonlinear program (NLP), we can generate several feasible sequences of foot placements and contact forces to avoid failure cases. We verified feasibility with demonstrations on the hardware SiLVIA, a sixlegged robot capable of vertically climbing between two walls by bracing itself in-between using only friction.

Current rigid linkage grippers are limited in flexibility, and gripper design optimality relies on expertise, experiments, or arbitrary parameters. Our proposed rigid gripper can accommodate irregular and off-center objects through a whippletree mechanism, improving adaptability. We present a whippletree-based rigid under-actuated gripper and its parametric design multi-objective optimization for a one-wall climbing task. Our proposed objective function considers kinematics and grasping forces simultaneously with a mathematical metric based on a model of an object environment. Our multi-objective problem is formulated as a single kinematic objective function with auto-tuning force-based weight. Our results indicate that our proposed objective function determines optimal parameters and kinematic ranges for our under-actuated gripper in the task environment with sufficient grasping forces.

In this paper, we introduce the Surface Material Dataset for Robotics Applications (SMDRA), a collection of RGB color image, depth data, and pixel-wise friction coefficient data of 10 different materials for computer vision research specifically with robotics applications in mind that require physical contact between the robot and its environment such as robotic manipulators or walking robots. These selected surface materials are both easily accessible around our daily lives and cover a wide range of friction coefficients. Our dataset is unique in that while there is an abundance of RGB-D data due to the popularization of imaging sensors, additional pixel-wise aligned data of a different modality are not readily available. The depth data is collected by an active stereo camera which has shown promise on a variety of different robotic applications, especially in outdoor environments. In addition, this dataset is greatly expanded with the addition of friction coefficient data. Similarly to humans, this additional information can be helpful in ensuing proper decision making in tasks ranging from grasping orientation and strength to path determination in an unstructured environment. A newly developed friction measuring device was used to obtain this data. We verify that existing Convolutional Neural Network (CNN) architectures, the Fully Convolutional Network (FCN) and U-Net, can be trained on the SMDRA. This result demonstrates that the SMDRA can be utilized to train a neural network model for segmentation and these different modes are not just additional information, but valuable modes that researchers can incorporate and exploit when applying computer vision algorithms on robotic platforms.

A mobility mechanism for robots to be used in tight spaces shared with people requires it to have a small footprint, to move omnidirectionally, as well as to be highly maneuverable. However, currently there exist few such mobility mechanisms that satisfy all these conditions well. Here we introduce Omnidirectional Balancing Unicycle Robot (OmBURo), a novel unicycle robot with active omnidirectional wheel. The effect is that the unicycle robot can drive in both longitudinal and lateral directions simultaneously. Thus, it can dynamically balance itself based on the principle of dual-axis wheeled inverted pendulum. This paper discloses the early development of this novel unicycle robot involving the overall design, modeling, and control, as well as presents some preliminary results including station keeping and path following. With its very compact structure and agile mobility, it might be the ideal locomotion mechanism for robots to be used in human environments in the future.

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visualinertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point.

Motion planning trajectories for a multi-limbed robot to climb up walls requires a unique combination of constraints on torque, contact force, and posture. This paper focuses on motion planning for one particular setup wherein a six-legged robot braces itself between two vertical walls and climbs vertically with end effectors that only use friction. Instead of motion planning with a single nonlinear programming (NLP) solver, we decoupled the problem into two parts with distinct physical meaning: torso postures and contact forces. The first part can be formulated as either a mixed-integer convex programming (MICP) or NLP problem, while the second part is formulated as a series of standard convex optimization problems. Variants of the two wall climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls, help verify the proposed method in simulation and experimentation.

This paper presents a complete motion planning approach for quadruped locomotion across an unknown terrain using a framework based on mixed-integer convex optimization and visual feedback. Vision data is used to find convex polygons in the surrounding environment, which acts as potentially feasible foothold regions. Then, a goal position is initially provided, which the best feasible destination planner uses to solve for an actual feasible goal position based on the extracted polygons. Next, a footstep planner uses the feasible goal position to plan a fixed number of footsteps, which may or may not result in the robot reaching the position. The center of mass (COM) trajectory planner using quadratic programming is extended to solve for a trajectory in 3D space while maintaining convexity, which reduces the computation time, allowing the robot to plan and execute motions online. The suggested method is implemented as a policy rather than a path planner, but its performance as a path planner is also shown. The approach is verified on both simulation and on a physical robot, ALPHRED, walking on various unknown terrains.

This paper presents a multi-functioning light weight robotic system, the Autonomous Legged Personal Helper Robot with Enhanced Dynamics (ALPHRED), capable of both locomotion and manipulation. In addition, we extended a 2D zero moment point (ZMP) trajectory optimization (TO) algorithm to a 3D implementation. As well as adding the acceleration of the center of mass to the TO cost in order to smooth out the motion of the robot during trajectories with support polygons that do not intersect. By implementing this versatile TO algorithm on a multi-modal robotic platform we showed that many different forms of stable locomotion and manipulation were possible including a dynamic 0.7 m/s trot gait.

This paper presents a technique to model statically indeterminate forces based on stiffness matrices for multi-limbed climbing robots. Current wall climbing robots in literature overlook statically indeterminate forces, causing an incapability to estimate climbing failure under certain circumstances. Accounting for these forces, robot deformation can be approximated, paving the way for the proposed two-wall climbing approach. During a wall climb, two failure modes, slide and over-torque, are identified to compute feasible climbing region. A hexapod robot is used to verify the proposed technique by climbing between walls with pure friction end effectors.

Embedding the dynamics of the Spring Loaded Inverted Pendulum (SLIP) and applying a compositional controller around it can simplify dynamic legged robot locomotion control, but what is the energetic cost of this convenience? This paper measures the magnitude of this effect in such a way that the results are applicable to a wide class of jumping robots. A three-link monoped model with revolute joints is used to compare the energetic costs of locomotion using two different control approaches: 1) SLIP-embedding with a Raibert-style controller optimized for energetic efficiency, and 2) a trajectory optimized only for energetic efficiency. By performing this comparison in simulation for a large number of different monopeds randomly sampled from a space of realistic robot designs, it is found that the SLIP-Raibert approach requires, on average, almost twice the energy of the trajectory-optimized controller to traverse a given distance. Furthermore, the increase in energetic cost does not depend much on the particulars of the robot design, as the SLIP-Raibert approach requires at least 50% more energy for approximately 88% of realistic robot designs.

The Non-Anthropomorphic Biped (NABi) has demonstrated agile motion without complex control due to its unique leg alignment in the sagittal plane. This paper presents NABi-S, a small version of NABi. The “S” designation refers to the new spring or “soleus” mechanism used to store energy and stabilize walking. An important feature of NABi-S is its ability to self-stabilize - it can recover from any tipping in the sagittal plane. Using this feature, NABi-S demonstrates stable motion using CPG control, a difficult task to achieve on a biped. In this paper, the soleus prototype is designed and analyzed. An open-loop, CPG algorithm is then implemented, and a parameter space search is performed to optimize robot locomotion.

This paper presents a compliant locomotion framework for torque-controlled humanoids using model-based whole-body control. In order to stabilize the centroidal dynamics during locomotion, we compute linear momentum rate of change objectives using a novel time-varying controller for the Divergent Component of Motion (DCM). Task-space objectives, including the desired momentum rate of change, are tracked using an efficient quadratic program formulation that computes optimal joint torque setpoints given frictional contact constraints and joint position / torque limits. In order to validate the effectiveness of the proposed approach, we demonstrate push recovery and compliant walking using THOR, a 34 DOF humanoid with series elastic actuation. We discuss details leading to the successful implementation of optimization-based whole-body control on our hardware platform, including the design of a “simple” joint impedance controller that introduces inner-loop velocity feedback into the actuator force controller.

This paper provides an overview of the embedded joint-space control approach developed for THOR, a new series elastic humanoid. The 60 kg robot features electromechanical linear series elastic actuators (SEAs), enabling low-impedance control of each joint in the lower body via linear to rotary and parallel mechanisms. We present a distributed joint impedance control framework that leverages a custom dual-axis motor controller to track position, velocity, and torque setpoints for each pair of joints. The required actuator forces are tracked using an inner force control loop combining feedforward and PID control with a model-based disturbance observer (DOB). Unlike previous approaches, we utilize an inverse plant model based on the open-loop actuator dynamics to simplify tuning of the cascaded controller by decoupling DOB estimates from the inner loop gains. The effectiveness of the proposed approach is verified through trajectory tracking and dynamic walking experiments conducted on the THOR humanoid utilizing a complementary optimization-based whole-body controller.

Series elastic actuators are frequently modeled using a conventional lumped mass model which has remained mostly unchanged since their introduction almost two decades ago. The lumped model has served well for early development but more descriptive models are now needed for new actuator designs and control approaches. In this paper we propose a new unlumped model specifically for linear series elastic actuators which uses a rack & pinion conceptualization to intuitively depict the mechanics of a linear ball screw drive. Results from hardware experiments are presented and compared to the predicted simulation results for both the conventional model and the new unlumped model. The results demonstrate that the new unlumped model is significantly more representative of the true actuator dynamics.

This work presents a method for deriving a gain scheduled balance controller to stabilize the gate of a three legged under-actuated robotic platform called THALeR (Tri-Pedal Hyper Altitudinal Legged Robot). The scheduler adapts the controller gains in real time based upon the system's instantaneous potential energy in order to create a smooth, stable gait. The final controller is simulated with white noise and impulse perturbations to show robustness.

Major rule changes for the RoboCup Humanoid League in 2015 pose significant vision and locomotion challenges for disambiguating similarly colored objects and navigating soft terrain. These significant changes highlight the need for applying general purpose humanoid robotics approaches that can handle abrupt environment modifications, and we utilize the general purpose THOR (Tactical Hazardous Operations Robot) series of robot from the recent DARPA Robotics Challenge (DRC). Specific techniques for vision, kicking and autonomy complement software developed for robust deployments in the DRC. In this paper, we present these soccer playing techniques, which were validated in the Humanoid AdultSize league in Hefei. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Developing a reliable humanoid robot that operates in uncharted real-world environments is a huge challenge for both hardware and software. Commensurate with the technology hurdles, the amount of time and money required can also be prohibitive barriers. This paper describes Team THOR's approach to overcoming such barriers for the 2013 DARPA Robotics Challenge (DRC) Trials. We focused on forming modular components - in both hardware and software - to allow for efficient and cost effective parallel development. The robotic hardware consists of standardized and general purpose actuators and structural components. These allowed us to successfully build the robot from scratch in a very short development period, modify configurations easily and perform quick field repair. Our modular software framework consists of a hybrid locomotion controller, a hierarchical arm controller and a platform-independent operator interface. These modules helped us to keep up with hardware changes easily and to have multiple control options to suit various situations. We validated our approach at the DRC Trials where we fared very well against robots many times more expensive.

Bipedal humanoid robots will fall under unforeseen perturbations without active stabilization. Humans use dynamic full body behaviors in response to perturbations, and recent bipedal robot controllers for balancing are based upon human biomechanical responses. However these controllers rely on simplified physical models and accurate state information, making them less effective on physical robots in uncertain environments. In our previous work, we have proposed a hierarchical control architecture that learns from repeated trials to switch between low-level biomechanically-motivated strategies in response to perturbations. However in practice, it is hard to learn a complex strategy from limited number of trials available with physical robots. In this work, we focus on the very problem of efficiently learning the high-level push recovery strategy, using simulated models of the robot with different levels of abstraction, and finally the physical robot. From the state trajectory information generated using different models and a physical robot, we find a common low dimensional strategy for high level push recovery, which can be effectively learned in an online fashion from a small number of experimental trials on a physical robot. This learning approach is evaluated in physics-based simulations as well as on a small humanoid robot. Our results demonstrate how well this method stabilizes the robot during walking and whole body manipulation tasks.

During heavy work, humans utilize whole body motions in order to generate large forces. In extreme cases, exaggerated weight shifts are used to impart large impact forces. There have been approaches to design stable whole body impact motions based on precise dynamic models of the robot and the target object, but they have practical limitations as the uncertainty in the ensuing reaction forces can lead to instability. In the current work, we describe a motion controller for a humanoid robot that generates impacts at an end effector while keeping the robot body balanced before and after the impact. Instead of relying on the accuracy of the impact dynamics model, we use a simplified model of the robot and biomechanically motivated push recovery controllers to reactively stabilize the robot against unknown perturbations from the impact. We demonstrate our approach in physically realistic simulations, as well as experimentally on a small humanoid robot platform.

Dynamic bipedal walking is susceptible to external disturbances and surface irregularities, requiring robust feedback control to remain stable. In this work, we present a practical hierarchical push recovery strategy that can be readily implemented on a wide range of humanoid robots. Our method consists of low level controllers that perform simple, biomechanically motivated push recovery actions and a high level controller that combines the low level controllers according to proprioceptive and inertial sensory signals and the current robot state. Reinforcement learning is used to optimize the parameters of the controllers in order to maximize the stability of the robot over a broad range of external disturbances. The controllers are learned on a physical simulation and implemented on the Darwin-HP humanoid robot platform, and the resulting experiments demonstrate effective full body push recovery behaviors during dynamic walking.

Bipedal walking in human environments is made difficult by the unevenness of the terrain and by external disturbances. Most approaches to bipedal walking in such environments either rely upon a precise model of the surface or special hardware designed for uneven terrain. In this paper, we present an alternative approach to stabilize the walking of an inexpensive, commercially-available, position-controlled humanoid robot in difficult environments. We use electrically compliant swing foot dynamics and onboard sensors to estimate the inclination of the local surface, and use a online learning algorithm to learn an adaptive surface model. Perturbations due to external disturbances or model errors are rejected by a hierarchical push recovery controller, which modulates three biomechanically motivated push recovery controllers according to the current estimated state. We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial DARwIn-OP small humanoid robot. Experimental results show that this combined approach enables the robot to walk over unknown, uneven surfaces without falling down.

Inspired by the pseudopod mobility mechanism found in amoebas, we propose a toroidal robot which can fold in on itself to generate the same overall motion of the amoeba. One of the advantages of such a robot is its ability to squeeze under obstacles and through holes smaller than its nominal diameter. These abilities make it particularly well suited for unstructured and highly constrained environments such as medical and search and rescue applications. We present several actuation mechanisms which are being investigated towards the development of an everting toroidal robot. For smaller scale applications, we present contracting ring actuators made up of shape memory alloy rings or electroactive polymer rings that create a differential stress and drive the motion. For larger scale applications, we present a tape spring mechanism which uses a membrane composed of treads arranged in a circular pattern. Another large scale mechanism uses a snake-like robot that can be formed into a torus which can ascend and descend cylindrical structures. We also present a chemical actuation method which utilizes chemically induced swelling in crosslinked polymers to produce forward motion. Finally, we describe a novel torus shaped actuator being developed which uses shape memory alloy rings to generate an everting motion.

IMPASS (Intelligent Mobility Platform with Active Spoke System) is a novel mobile robot driven by two rimless spoke wheels. Each of the spokes can be individually actuated with intelligent motion planning to walk over uneven terrain with high mobility. This form of novel locomotion has the potential to combine the efficiency of a wheeled robot and the mobility of a legged robot. A highly mobile robot such as IMPASS could prove very valuable in applications where the terrain is complex and dangerous, such as search and rescue, reconnaissance, or anti-terror response. This video presents an overview of the system concept with examples and demonstrations of its unique mobility. Beginning with an overview of the system concept and hardware, the video then demonstrates the different mobility advantages. These include rough terrain locomotion, dynamic surface locomotion, and large step climbing.

Intelligent mobility platform with active spoke system (IMPASS) is a novel wheel-leg hybrid robot that can walk in unstructured environments by stretching in or out three independently actuated spokes of each wheel. This form of novel locomotion has the potential to combine the efficiency of a wheeled robot and the mobility of a legged robot. A highly mobile robot such as IMPASS could prove very valuable in applications where the terrain is complex and dangerous, such as search and rescue, reconnaissance, or anti-terror response. This video presents the experiments and findings of a variety of straight-line walking, transitions, and turning gaits. With the 1-1 and 2-2 straight walking gaits, the robot can move forward stably but with different constraints. Unlike other wheeled vehicles which use Arckerman steering or differential steering, IMPASS can implement novel turning gaits even though the left and right hubs rotate with the same angular velocity. Steady state turning gait is demonstrated with skew transitions. Additionally, a free form gait is demonstrated using joystick control with interesting observations.

STriDER (Self-Excited Tripedal Dynamic Experimental Robot) is a novel three-legged walking machine that exploits the concept of actuated passive dynamic locomotion to dynamically walk with high energy efficiency and minimal control. Unlike other passive dynamic walking machines, this unique tripedal locomotion robot is inherently stable with its tripod stance, can change directions, and is relatively easy to implement, making it practical to be used for real life applications. STriDER begins its step with a stable stance like a camera tripod. As the center of gravity of the robot shifts forward pass the "pivot line" defined by the two feet of the stance legs, the robot begins to fall in the direction perpendicular to the pivot line. The middle leg naturally swings between the two stance legs using the concept of actuated passive dynamic locomotion. The swing leg then catches the fall and the robot resets to its original tripod posture in preparation for its next step. STriDER can easily change its direction of walking, simply by changing the sequence of choice of the swing leg and the stance legs. In this video, we present the concept of this novel walking machine and the mechanical design of the first prototype. The results from the dynamic simulation and a simple experiment for a single step are presented for comparison

The Dynamic Anthropomorphic Robot with Intelligence (DARwIn), a humanoid robot, is a sophisticated hardware platform used for studying bipedal gaits that has evolved over time. Three version of DARwIn have been developed, each an improvement on its predecessor. The first version, DARwIn 0.0, was used as a design study to determine the feasibility of creating a small-scale humanoid robot. The second version, DARwIn 1.0, used improved gaits and software. The third version, DARwIn 2.0 has not only even better gaits and control software, but also has artificial intelligence. The platform is primarily used as a research tool for studying bipedal and humanoid locomotion. Additionally, DARwIn 2.0 has been tailored for the international autonomous robot soccer competition, Robocup.