Humanoid robots are increasingly being developed for seamless interaction with humans in diverse domains, yet generating expressive and physically-feasible motions remains a core challenge. We propose a robust and automated pipeline for motion retargeting that enables the generation of natural, stable, and highly expressive motions for a wide variety of humanoid robots using different motion data sources, including noisy pose estimations. To ensure robustness, our approach unifies motions from different kinematic structures into a common canonical rig, systematically refines the motion trajectory to address infeasible poses, enforces foot-contact constraints, and enhances stability. The retargeted motion is then refined to closely follow the source motion while respecting each robot’s physical limits. Through extensive experiments on 12 simulated robots and validation on three real robots, we show that our methodology reliably produces expressive upper-body movements with consistent foot contact. This work represents an important step towards automating robust and expressive motion generation for humanoid robots, enabling deployment in various real-world scenarios.

Indoor parking lots are the GPS-denied spaces to which vision-based localization approaches have usually been applied to solve localization problems. However, due to the repetitiveness and symmetry of the spaces, visual localization methods commonly confront difficulties in estimating precise 3D poses. In this study, we propose four novel modules that improve localization precision by imposing the existing methods with the spatial discerning ability. The first module constructs a key text graph that represents the topology of key texts in the space and becomes the basis for discerning repetitiveness and symmetry. Next, the orientation filtering module estimates the unknown 3D orientation of the query image and resolves spatial symmetric ambiguity. The similarity scoring module sorts out the top-scored database images, discerning the spatial repetitiveness based on detected key text bounding boxes. Our pose verification module evaluates the pose confidence of top-scored candidates and determines the most reliable pose. Our method has been validated in two real indoor parking lots, achieving new state-of-the-art performance levels.

Soft tactile sensors can enable robots to grasp objects easily and stably by simultaneously providing tactile data and mechanical compliance to robotic hands. If there are low-cost and easy-to-build robotic hands equipped with soft tactile sensors, they would be highly accessible and facilitate many robotics projects. To this end, we propose an accessible robot hand capable of tactile sensing, which can be produced through digital fabrication. We made the robot hand using commercial servo motors as well as components 3D printed from PETG, TPU, and conductive TPU. These materials allow the robot hand to have a soft, durable, and even functional structure. Specifically, the soft fingertip was crafted from TPU and conductive TPU, and their mechanical and electrical properties enable easy implementation of tactile sensing capabilities, such as force and capacitive touch, simply by adding off-the-shelf sensors (air-pressure and capacitance). The proposed robot hand could effectively sense interaction forces and proximity to conductive objects, and its utilization in various tasks was also demonstrated successfully.

When designing the motions of wearable robotic systems, physical Human-Robot Interaction (pHRI) is a crucial consideration. This study suggests a coordinated motion planner for Supernumerary Robotic Limbs (SRLs) to minimize the applied moment from the robotic system’s operation to the human body. Given task motion trajectories, moment compensation motions are generated to reduce the asymmetric moment elements while maintaining the other element below a nominal value. The performance of the motion planner is evaluated by the simulation and the hardware experiment. An object pick-and-place scenario is used as a goal task. The simulation results demonstrate that under different conditions, the compensation motions significantly decrease the generated roll and yaw moments when compared to the results without the motions. The hardware experiment validates its practicality as a real-time motion controller of a physical robotic system.

This paper presents the development and evaluation of Ringbot, a novel leg-wheel transformer robot incorporating a monocycle mechanism with legs. Ringbot aims to provide versatile mobility by replacing the driver and driving components of a conventional monocycle vehicle with legs mounted on compact driving modules inside the wheel. The paper covers the hardware and software implementation of a prototype robot. The Ringbot prototype features a wheel and two driving modules located inside, each equipped with a 3-DoF leg for balancing, steering, and legged motions to assist monocycle driving. The driving control is achieved through decoupled speed controller and steering controller. In addition, active-legged motions are implemented and managed through a finite-state machine. The controllers for wheeled driving and legged motions were tested in a simulation environment, as well as on the hardware prototype, to verify the concept of a monocycle with legs and evaluate the prototype's capabilities.

Although many soft robotic skins have been introduced, their use has been hindered due to practical limitations such as difficulties in manufacturing, poor accessibility, and cost inefficiency. To solve this, we present a low-cost, easy-to-build soft robotic skin utilizing air-pressure sensors and 3D-printed pads. In our approach, we utilized digital fabrication and ROS to facilitate the creation and use of the robotic skin. The skin pad was fabricated by printing thermoplastic urethane (TPU) and post-processed with an organic solvent to secure air-tightness. Each pad consists of a TPU shell and infill, so the internal air pressure changes in response to tactile stimuli such as force and vibration. The internal pressure is measured and processed by a microcontroller and transmitted to the PC via a serial bus. We conducted experiments to investigate the characteristics of the skin pads, and the results showed that the developed robotic skins are capable of perceiving interaction force and dynamic stimuli. Finally, we developed the dedicated soft robotic skins for our custom robot designed in-house, and demonstrated safe and intuitive physical human-robot interaction.

In this paper, we propose a lip-inspired passive jamming gripper by mimicking teeth structures from a dog's oral structure. Animal lips are hydrostatic structures. To mimic the features, which are usually soft but rigid when contacted, we used the passive particle jamming effect. To increase the adaptability of our previous gripper to grasp various shaped objects, we focused on the dogs' oral structure and holding behaviors. Dogs use spaces inside their mouths to hold sticks firmly when moving. The grasping force of the upgraded gripper was improved by generating teeth structures with the mimicked lip structures. Experiments were conducted to demonstrate the grasping ability of the gripper with the teeth structures by comparing it to other types of grippers with cylindrical and cuboid objects of various dimensions. We also showed that the proposed gripper could hold daily kitchen objects better than the previous version gripper by using closing lip-pouches.

When introducing robotic systems to home environments, there are several aspects to consider such as accessibility to robotic systems and interaction capability with human subjects. Thus, in this paper, a 3-DOF robotic sensor module is suggested to embrace the concerns. It includes a handy plug-and-play feature to use one module in different spots at home. In addition, it delivers non-verbal, non-display communicative cues through human-like head motions while it is plugged into docking mounts with different states, including angled or moving. It also can track human subjects to detect and focus on users who try to interact with the robot. The performance of the robotic module was evaluated, and its compatibility with different systems was demonstrated.

In this paper, we present an add-on system that enhances the capabilities of a quadrupedal robot. The addon system efficiently allows the integration of two 6-DOF manipulators with a quadruped as a single system. The design of the system is developed with modularity as an important principle, allowing for versatility and adaptability in various applications of mobile manipulation. With the modular design, the system can easily be used for mobile-manipulation tasks but also as a system for human entertainment. We show the modular and versatility of the system through applications in a home setting and various entertainment settings. The proposed system leads to an enhanced level of human-robot interaction with more engaging and interactive experiences.

Indoor visual localization is significant for various applications such as autonomous robots, augmented reality, and mixed reality. Recent advances in visual localization have demonstrated their feasibility in large-scale indoor spaces through coarse-to-fine methods that typically employ three steps: image retrieval, pose estimation, and pose selection. However, further research is needed to improve the accuracy of large-scale indoor visual localization. We demonstrate that the limitations in the previous methods can be attributed to the sparsity of image positions in the database, which causes view-differences between a query and a retrieved image from the database. In this paper, to address this problem, we propose a novel module, named pose correction, that enables re-estimation of the pose with local feature matching in a similar view by reorganizing the local features. This module enhances the accuracy of the initially estimated pose and assigns more reliable ranks. Furthermore, the proposed method achieves a new state-of-the-art performance with an accuracy of more than 90 % within 1.0 m in the challenging indoor benchmark dataset InLoc for the first time.1

In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a projection-invariant mapping to handle the different expressivity of humans and humanoid robots. Furthermore, our method guarantees that the generated robot pose is collision-free and satisfies position limits by utilizing nonparametric regression in the shared latent space. We demonstrate that our method can generate expressive robotic motions from both the CMU motion capture database and YouTube videos.

Underactuated robots often require involved routines for trajectory planning due to their complex dynamics. Flapping-wing aerial vehicles have unsteady aerodynamics and periodic gaits that complicate the planning procedure. In this paper, we improve upon existing methods for flight planning by introducing a two-stage optimization routine to plan flapping flight trajectories. The first stage solves a trajectory optimization problem with a data-driven fixed-wing approximation model trained with experimental flight data. The solution to this is used as the initial guess for a second stage optimization using a flapping-wing model trained with the same flight data. We demonstrate the effectiveness of this approach with a bat robot in both simulation and experimental flight results. The speed of convergence, the dependency on the initial guess, and the quality of the solution are improved, and the robot is able to track the optimized trajectory of a dive maneuver.

Bat flight has been an underdeveloped area of bio-inspired robotics because of the vast complexities of biological bat flight and the over 40 degrees of freedom present in their bodies. The robotic flapping system Bat Bot (B2) has been shown to exhibit fundamental properties of biological bat flight with its articulated wings, its deformable membrane, and its controllable hindlimbs. However, the system is limited in performance by its relatively large mass for the thrust it produces. In an effort to further pursue this important area of flapping flight, we have made several important hardware improvements to the system based on biological inspiration. These include passive wrist joints to reduce negative lift in the upstroke and a novel elastic fiber membrane to mimic the anistropic nature of bat skin for performance and durability. The redesigned flapping mechanism and structure have reduced the weight by 22%, increased the flapping amplitude, lowered mechanical slackness, and improved mass distribution. These hardware improvements are functional together in free-flight tests. This new system Bat Bot 2.0 (B2.0) provides insights into the important elements of design of bat robots, and it brings the goal of complex bat flight maneuvers closer to reality.

In this paper, we present the second version of a reconfigurable modular legged robot, Snapbot V2. The mechanical design of Snapbot V2 is enhanced for better dynamic performance and robust connection with modular legs. A motion generator for locomotion is developed to achieve various locomotion skills in one to six-leg configurations. The locomotion is tested on a multi-body dynamic simulation model and implemented on a physical robot as well. A visual detection is implemented with a camera module to recognize the robot's configuration. By detecting the particular color of the parts at the leg module, the robot can recognize the number and location of the connected legs. Based on the recognized configuration, Snapbot V2 selects the proper locomotion style automatically.

This paper describes the development of a system for lifelike gaze in human-robot interactions using a humanoid Audio-Animatronics®bust. Previous work examining mutual gaze between robots and humans has focused on technical implementation. We present a general architecture that seeks not only to create gaze interactions from a technological standpoint, but also through the lens of character animation where the fidelity and believability of motion is paramount; that is, we seek to create an interaction which demonstrates the illusion of life. A complete system is described that perceives persons in the environment, identifies persons-of-interest based on salient actions, selects an appropriate gaze behavior, and executes high fidelity motions to respond to the stimuli. We use mechanisms that mimic motor and attention behaviors analogous to those observed in biological systems including attention habituation, saccades, and differences in motion bandwidth for actuators. Additionally, a subsumption architecture allows layering of simple motor movements to create increasingly complex behaviors which are able to interactively and realistically react to salient stimuli in the environment through subsuming lower levels of behavior. The result of this system is an interactive human-robot experience capable of human-like gaze behaviors.

In this paper, we propose a trajectory-based reinforcement learning method named deep latent policy gradient (DLPG) for learning locomotion skills. We define the policy function as a probability distribution over trajectories and train the policy using a deep latent variable model to achieve sample efficient skill learning. We first evaluate the sample efficiency of DLPG compared to the state-of-the-art reinforcement learning methods in simulated environments. Then, we apply the proposed method to a four-legged walking robot named Snapbot to learn three basic locomotion skills of turn left, go straight, and turn right. We demonstrate that, by properly designing two reward functions for curriculum learning, Snapbot successfully learns the desired locomotion skills with moderate sample complexity.

Imitation of the upper body motions of human demonstrators or animation characters to human-shaped robots is studied in this paper. We present a pipeline for motion retargeting by transferring the joints of interest (JOI) of source motions to the target humanoid robot. To this end, we deploy an optimization-based motion retargeting method utilizing link length modifications of the source skeleton and a task (Cartesian) space fine-tuning of JOI motion descriptors. To evaluate the effectiveness of the proposed pipeline, we use two different 3-D motion datasets from three human demonstrators and an Ogre animation character, Bork, and successfully transfer the motions to four different humanoid robots: DARwIn-OP, COmpliant HuMANoid Platform (COMAN), THORMANG, and Atlas. Furthermore, COMAN and THORMANG are actually controlled to show that the proposed method can be deployed to physical robots.

This paper proposes a realistic indoor modeling framework for large-scale indoor spaces. The proposed framework reduces the geometric complexity of an indoor model to efficiently represent large-scale environments for image-based rendering (IBR) approaches. For this purpose, the proposed framework removes geometrically excluded objects (GEOs) in point cloud and images, which represent the primary factors in high geometric complexity. In particular, GEOs are coherently removed from all images using a global geometry model. Then, the remaining holes are inpainted using globally consistent guidelines, to achieve accurate image blending in IBR approaches. The experimental results verify that the proposed GEO removal framework provides efficient point clouds and images for realistic indoor modeling in large-scale indoor spaces.

This paper presents a 6 DOF leg mechanism for a humanoid robot. The proposed Hybrid Leg is designed to combine serial and parallel mechanisms and consists of a pair of twin 3 DOF serial chains in parallel. A 5-bar-linkage mechanism is implemented to the serial mechanism to generate 2 DOF motion regarding hip and knee pitch rotation. The hardware prototype is designed by matching the kinematic specification of a commercial robot's leg to compare the proposed mechanism with a conventional serial leg. We derive the analytical expressions of its forward and inverse kinematics. End-effector workspaces are shown with plots and inverse dynamics analysis of Hybrid Leg and serial leg with a given walking gait trajectory is presented. Hardware experiment is conducted with a prototype to verify the simulated workspace and trajectory tracking performance.

In this paper, we present the design and performance evaluation of a bipedal robot that utilizes the Hybrid Leg mechanism. It is a leg mechanism that achieves 6 DOF with a combined structure of serial and parallel mechanism. It is designed to have a light structural inertia and large workspace for agile bipedal locomotion. A new version of Hybrid Leg is fabricated with carbon fiber tubes and bearings to improve its structural rigidity and accuracy while supporting its weight. A pair of Hybrid Legs is assembled together for bipedal locomotion. In the assembly, we adopt a pelvis structure with an yaw angle offset to enlarge the feet workspace, inspired by the toe-out angle of the human feet. The workspace and range of velocity are presented in simulation and verified with hardware experiments. We also demonstrate a simple forward walking motion with the developed robot.

We present a novel computational approach to design the robotic devices from high-level motion specifications. Our computational system uses a library of modular components-actuators, mounting brackets, and connectors-to define the space of possible robot designs. The process of creating a new robot begins with a set of input trajectories that specify how its end effectors and/or body should move. By searching through the combinatorial set of possible arrangements of modular components, our method generates a functional, as-simple-as-possible robotic device that is capable of tracking the input motion trajectories. To significantly improve the efficiency of this discrete optimization process, we propose a novel heuristic that guides the search for appropriate designs. Briefly, our heuristic function estimates how much an intermediate robot design needs to change before it becomes able to execute the target motion trajectories. We demonstrate the effectiveness of our computational design method by automatically creating a variety of robotic manipulators and legged robots. To generate these results, we define our own robotic kit that includes off-the-shelf actuators and 3-D printable connectors. We validate our results by fabricating two robotic devices designed with our method.

We develop a reconfigurable legged robot, named Snapbot, to emulate configuration changes and various styles of legged locomotion. The body of Snapbot houses a microcontroller and a battery for untethered operation. The body also contains connections for communication and power to the modular legs. The legs can be attached to and detached from the body using magnetic mechanical couplings. In the center of this coupling, there is a multi-pin spring-loaded electrical connector that distributes power and transmits data between the controller and leg actuators. The locomotion algorithm is implemented on the microcontroller. The algorithm enables Snapbot to locomote in various configurations with one to six legs by recognizing configuration changes and selecting the locomotion method according to the current configuration. Snapbot will be utilized for further research on legged locomotion.

Among legged robots, hopping and running robots are useful because they can traverse terrain at high speeds and are a benchmark platform for locomotion actuators; if an actuator can power a hopping robot, it can power a walking robot. We aim to create a hopping mechanism for a small-scale, one-legged, untethered hopping robot. A parallel-elastic actuator is an efficient way to do this, and enables the actuator to directly inject energy into the spring, but requires a high-speed, low-inertia actuator. Voice coil actuators are electrically-powered direct-drive translational motors that have very low moving inertia, low friction, can produce force at high speeds, and have a linear force output. These qualities make them ideal candidate motors for a linear elastic actuator in parallel (“LEAP”). Here, we derive an electromechanical model of the LEAP mechanism, develop a simple bang-bang hopping controller, and simulate hopping with a range of spring parameters to find an optimal spring stiffness that maximizes hopping height. We detail our implemented design, and characterize its performance through a series of experiments. We test our robot with different spring stiffnesses, and demonstrate hopping at a maximum steady-state of 3.5 cm ground-clearance (approx. 20% leg length). Our results suggest that the LEAP mechanism may serve the weight-bearing functions of a robot leg.

To enable robust dynamic walking on the Atlas robot, we extend our previous work by adding a receding-horizon component. The new controller consists of three hierarchies: a center of mass (CoM) trajectory planner that follows a sequence of desired foot steps, a receding-horizon controller that optimizes the next foot placement to minimize future CoM tracking errors, and an inverse dynamics based full body controller that generates instantaneous joint commands to track these motions while obeying physical constraints. An approximate value function is generated by the CoM planner, and is used to guide the foot placement and inverse dynamics optimizations. The proposed controller is implemented and tested on the Atlas robot. It is capable of walking with strong external perturbations such as recovering from large pushes and traversing unstructured terrain.

The design of legged robots is often inspired by animals evolved to excel at different tasks. However, while mimicking morphological features seen in nature can be very powerful, robots may need to perform motor tasks that their living counterparts do not. In the absence of designs that can be mimicked, an alternative is to resort to mathematical models that allow the relationship between a robot's form and function to be explored. In this paper, we propose such a model to co-design the motion and leg configurations of a robot such that a measure of performance is optimized. The framework begins by planning trajectories for a simplified model consisting of the center of mass and feet. The framework then optimizes the length of each leg link while solving for associated full-body motions. Our model was successfully used to find optimized designs for legged robots performing tasks that include jumping, walking, and climbing up a step. Although our results are preliminary and our analysis makes a number of simplifying assumptions, our findings indicate that the cost function, the sum of squared joint torques over the duration of a task, varies substantially as the design parameters change.

Effective teleoperation requires real-time control of a remote robotic system. In this work, we develop a controller for realizing smooth and accurate motion of a robotic head with application to a teleoperation system for the Furhat robot head [1], which we call TeleFurhat. The controller uses the head motion of an operator measured by a Microsoft Kinect 2 sensor as reference and applies a processing framework to condition and render the motion on the robot head. The processing framework includes a pre-filter based on a moving average filter, a neural network-based model for improving the accuracy of the raw pose measurements of Kinect, and a constrained-state Kalman filter that uses a minimum jerk model to smooth motion trajectories and limit the magnitude of changes in position, velocity, and acceleration. Our results demonstrate that the robot can reproduce the human head motion in real time with a latency of approximately 100 to 170 ms while operating within its physical limits. Furthermore, viewers prefer our new method over rendering the raw pose data from Kinect.

We have developed a toy sized humanoid robot with soft air-filled modules on its links which sense contact and protect the robot and any interacting humans from damaging collisions. This robot, meant for robust physical interaction, is required to endure contact with children in the form of hugs and other playful interactions. It is therefore necessary to quantify the forces exerted during these interactions so that robots can be designed to both withstand these forces, as well as interact safely and intuitively in these situations. To quantify the range of forces exerted by children when performing both soft and strong hugs, we conducted a study in which 28 children (11 boys, 17 girls) between 4 and 10 years old hugged a pressure sensing doll while the pressure was recorded. We found a child's maximum expected hugging force (2.623 psi for our setup) during free play. The data gathered in this study will guide the further development of our physically interactive robot.

Our goal is to bring animation characters to life in the real world. We present a bipedal robot that looks like and walks like an animation character. We start from animation data of a character walking. We develop a bipedal robot which corresponds to lower part of the character following its kinematic structure. The links are 3D printed and the joints are actuated by servo motors. Using trajectory optimization, we generate an open-loop walking trajectory that mimics the character's walking motion by modifying the motion such that the Zero Moment Point stays in the contact convex hull. The walking is tested on the developed hardware system.

The purpose of this research is the development of a soft skin module with a built-in airtight cavity in which air pressure can be sensed. A pressure feedback controller is implemented on a robotic system using this module for contact sensing and gentle grasping. The soft skin module is designed to meet size and safety criteria appropriate for a toy-sized interactive robot. All module prototypes are produced using a muti-material 3D printer. Experimental results from collision tests show that this module significantly reduces the impact forces due to collision. Also, using the measured pressure information from the module, the robotic system to which these modules are attached is capable of very gentle physical interaction with soft objects.

We propose a control framework for dynamic bipedal locomotion with compliant joints. A novel 3D dynamic walking is achieved by utilizing natural dynamics of the system. It is done by 1) driving robot joints directly with the posture-based state machine and 2) controlling tendon-driven compliant actuators. To enlarge gait's basin attraction for stable walking, we also adaptively plan step-to-step motion and compensate stance/swing motion. Final joint input is described by a superposition of state machine control torques and compensation torques of balancers. Various walking styles are easily generated by composing straight and turning gait-primitives and such walking is effectively able to adapt on various environments. Our proposed method is applied to a torque controlled robot platform, Roboray. Experimental results show that gaits are able to traverse inclined and rough terrains with bounded variations, and the result gaits are human-like comparing the conventional knee bent walkers.

This paper gives an overview of the development of a novel biped walking machine for a humanoid robot, Roboray. This lower-limb robot is designed as an experimental system for studying biped locomotion based on force and torque controlled joints. The robot has 13 actuated DOF and torque sensors are integrated at all the joints except the waist joint. We designed a new tendon type joint modules as a pitch joint drive module, which is highly back-drivable and elastic. We also built a decentralized control system using the small controller boards named Smart Driver. The forward walking experiment with this lower limbs was conducted to test the mechanical structure and control system.

This paper presents a framework to generate dynamic walking for biped robots. A set of self-stable gait primitives is first constructed. It is done by 1) representing parametric gait primitives, 2) utilizing state-dependent torque control, and 3) doing numerical optimization that takes into account the complex multi-body dynamics with frictional contact forces. Dynamic walking to follow the arbitrary path including a curve is then generated online via sequentially composing primitive motions. Results show that dynamic gaits are humanlike and efficient compared to the conventional knee bent walkers. Our proposed method is applied to a torque-controlled, human-sized biped robot platform, `Roboray' which is cable-driven partially for joint compliance. Following a discussion on robot design and control, experimental results are also reported.

To achieve dynamic balancing and natural walking for a bipedal robot we propose a novel force-based control framework. Given 6-dimensional pose vector representing robot's posture and attitude, desired force and moment in the task space are computed. To generate the force and moment as desired, we propose the use of virtual gravity compensation (VGC), essentially a dynamic controller that outputs joint torques. By using the VGC-based balancing controller, the robot can maintain a desired pose stably even on a tilting plate. We also propose to extend the VGC-based balancing controller to implement a walking algorithm that controls the desired pose in terms of capture point using a finite state machine. The control algorithm was tested with torque-controlled humanoid platforms developed by our group to demonstrate robust and natural gaits under various walking environments. The robot walked robustly on irregular surfaces and recovered from external pushes. The robot also exhibited natural walking motions such as pendulum-like leg swings and heel-to-toe transitions, a characteristic feature of human gait, all without explicitly designating joint angle trajectories.

Bipedal walking robots are inherently hybrid systems due to their intermittent, switching dynamics resulting from the impact between the robot foot and the ground as the robot foot lands on the ground. It is well known that stable (passive) limit cycles for the biped robots can be induced on shallow slopes without actuation. Recently the studies in passive dynamic walking have considered the robots with knee and point or curved feet. In this paper, we study the passive dynamic walking for biped robots with knee and fixed flat feet, which includes heel and toe rocking motions and the effect of foot length on the passive limit cycles. We derive the dynamic equations of motion for this model. We show by simulation that the proposed robot model can walk down a slope passively and also verify the stability of this walking by calculating the eigenvalues of the Jacobian of the Poincarè map. By using a numerical search method, we find the initial conditions of the stable limit cycles for various slope angles and foot lengths.

We propose a balancing control framework for a torque-controlled biped robot, Roboray. Roboray has two 6 DOF legs and torque sensors are integrated at all the leg joints. It has a new cable-driven joint module as a pitch joint drive, which is highly back-drivable and elastic. Using these hardware characteristics, we propose a new balancing control algorithm. This algorithm is the combination of gravity compensation, virtual gravity control and damping control. A friction compensation technique is also introduced in order to eliminate the nonlinearity of damping and to improve the performance of torque tracking. Our proposed method is applied to a simple inverted pendulum system and Roboray. Experimental results show that these two system keep their balance when they are pushed slightly.