Running is a complex dynamic task that places strict requirements on both the physical components and software-control systems of a robot. This paper explores some of those requirements and, in particular, explores how a variable-compliance actuation system can satisfy many of them. We present the mechanical design and software-control system for such an actuator system. We analyze its performance through simulation and bench-top experimental validation of a prototype version. In conclusion, we demonstrate, through simulation, the application of our proof-of-concept actuator to the problem of biped running.

We present a method for interactively guiding the navigation of a humanoid robot through complex terrain via an intuitive path-drawing interface. In contrast to full autonomy or direct teleoperation of the robot, the user suggests an overall global navigation route by ¿drawing¿ a path onto the environment while the robot is walking. The path is used by a footstep planner that searches online for a sequence of suitable footstep locations that follow the indicated path as closely as possible while respecting the robot dynamics and overall navigation safety. In this way, the planner provides the robot partial autonomy in selecting precise footstep sequences while the human operator retains high-level control of the global navigation route. We present experimental results of the complete system on the biped humanoid HRP-2 navigating on and around various platforms, chairs, and stairs. We use an augmented reality system so that interactively drawing paths on the world is intuitive and natural.

We present an approach to navigating a biped robot safely and efficiently through a complicated environment of previously unknown obstacles and terrain using only on-board sensing and odometry. Sensing of the environment is performed by a pivoting laser scanner, which continues to update the terrain representation as the robot walks. Safe stepping motions are planned from this data to follow the user's command, given in the form of an end goal, a rough path, or a joystick input. Results are demonstrated on a prototype robot in several environments.

This paper discusses the design principles and philosophy of the BiMASC, a biped with mechanically adjustable series compliance which incorporates tuned mechanical leg springs. This robot will be capable of dynamic running using mechanical leg springs, as well as dynamic ballistic walking with human-like passive leg swing behavior. The BiMASC will enable the study of the role of both controllable compliance in running and will serve as a test platform for control strategies that utilize the leg springs and other natural dynamics of the robot. The mechanism is designed to behave in a dynamically "clean" manner, such that relatively simple mathematical models will accurately predict the robot's behavior. The availability of simple and accurate mathematical models will facilitate the design of controllers, accurate simulations, and the implementation of accurate model-based control on the robot.

Outside of the laboratory, accurate models of ground impact dynamics are either difficult or impossible to obtain. Instead, a rigid ground model is often used in gait and controller design, which simplifies the system model and allows attention to remain focused on other aspects of running. In real-world terrain this simplification may overlook important dynamic effects. Immediately following a foot touchdown event, sensitivity to ground stiffness is at its highest and at the same time the accuracies of state estimates are at their lowest. Even if ground stiffness is known and state estimates are accurate, actuator bandwidth limitations make immediate compensation difficult. Taking inspiration from nature, we propose a novel solution to attenuate the effects of unexpected ground stiffness changes using a unified control system comprised of hardware passive dynamics and open-loop software control policies.

We have equipped a Honda ASIMO humanoid with the ability to navigate autonomously in obstacle-filled environments. In addition to finding its way through known, fixed obstacle configurations, the planning system can reason about the future state of the world to locomote through challenging environments when the obstacle motions can be inferred from observation. This video presents work using a vision system to predict the velocities of objects in the scene, allowing ASIMO to safely navigate autonomously through a dynamic environment. Neither obstacle positions nor velocities are known at the start of the trial, but are estimated online as the robot walks. The planner constantly adjusts the footstep path with the latest estimates of ASIMO’s position and the obstacle trajectories, allowing the robot to successfully circumnavigate the moving obstacles.

For humanoid robots to fully realize their biped potential in a three-dimensional world and step over, around or onto obstacles such as stairs, appropriate and efficient approaches to execution, planning and perception are required. To this end, we have accelerated a robust model-based three-dimensional tracking system by programmable graphics hardware to operate online at frame-rate during locomotion of a humanoid robot. The tracker recovers the full 6 degree-of- freedom pose of viewable objects relative to the robot. Leveraging the computational resources of the GPU for perception has enabled us to increase our tracker's robustness to the significant camera displacement and camera shake typically encountered during humanoid navigation. We have combined our approach with a footstep planner and a controller capable of adaptively adjusting the height of swing leg trajectories. The resulting integrated perception-planning-action system has allowed an HRP-2 humanoid robot to successfully and rapidly localize, approach and climb stairs, as well as to avoid obstacles during walking.

Libraries of trajectories are a promising way of creating policies for difficult problems. However, often it is not desirable or even possible to create a new library for every task. We present a method for transferring libraries across tasks, which allows us to build libraries by learning from demonstration on one task and apply them to similar tasks. Representing the libraries in a feature-based space is key to supporting transfer. We also search through the library to ensure a complete path to the goal is possible. Results are shown for the Little Dog task. Little Dog is a quadruped robot that has to walk across rough terrain at reasonably fast speeds.

We present the concept of an "intelligent" joystick, an architecture which provides simple and intuitive high-level directional control of a legged robot while adjusting the actual foot placements autonomously to avoid stepping in undesirable places. The general concept can be likened to riding a horse: high-level commands are provided, while the "intelligence" of the underlying system selects proper foot placements with respect to the shape and properties of the underlying terrain and overall balance considerations. We demonstrate a prototype system used for realtime control of the humanoid robot HRP-2

As navigation autonomy becomes an increasingly important research topic for biped humanoid robots, efficient approaches to perception and mapping that are suited to the unique characteristics of humanoids and their typical operating environments are required. This paper presents a system for online environment reconstruction that utilizes both external sensors for global localization, and on-body sensors for detailed local mapping. An external optical motion capture system is used to accurately localize on-board sensors that integrate successive 2D views of a calibrated camera and range measurements from a SwissRanger SR-2 time-of-flight sensor to construct global environment maps in real-time. Environment obstacle geometry is encoded in 2D occupancy grids and 2.5D height maps for navigation planning. We present an on-body implementation for the HRP-2 humanoid robot that, combined with a footstep planner, enables the robot to autonomously traverse dynamic environments containing unpredictably moving obstacles

The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with a lossscaled margin) than any other policy for a given planning domain. We provide a novel approach, M M P B O O S T , based on the functional gradient descent view of boosting (Mason et al., 1999; Friedman, 1999a) that extends MMP by "boosting" in new features. This approach uses simple binary classification or regression to improve performance of MMP imitation learning, and naturally extends to the class of structured maximum margin prediction problems. (Taskar et al., 2005) Our technique is applied to navigation and planning problems for outdoor mobile robots and robotic legged locomotion.

Despite the recent achievements in stable dynamic walking for many humanoid robots, relatively little navigation autonomy has been achieved. In particular, the ability to autonomously select foot placement positions to avoid obstacles while walking is an important step towards improved navigation autonomy for humanoids. We present a footstep planner for the Honda ASIMO humanoid robot that plans a sequence of footstep positions to navigate toward a goal location while avoiding obstacles. The possible future foot placement positions are dependent on the current state of the robot. Using a finite set of state-dependent actions, we use an A* search to compute optimal sequences of footstep locations up to a time-limited planning horizon. We present experimental results demonstrating the robot navigating through both static and dynamic known environments that include obstacles moving on predictable trajectories.

This paper describes an online system for footstep planning using a 3D map reconstructed by visual odometry. This system consists of two key components: 3D reconstruction via visual odometry from a stereo image sequence to obtain a dense local world model, and a footstep planner for biped robots using the reconstructed 3D map. Visual odometry is a method to connect 3D image sequences to obtain 6DOF camera motion and dense 3D environment information. The method described in this paper consists of three components: stereo depth map calculation, 3D flow calculation from tracking raw image features, and 6DOF camera motion estimation from RANSAC. Using the resulting 3D data, an optimal sequence of footstep locations is planned. The footstep planner is provided a height map of the terrain and a discrete set of possible footstep motions. The planner then evaluates footstep locations for viability using a collection of heuristic metrics designed to encode the relative safety, effort required, and overall motion complexity. Finally, we implemented this system on the humanoid robot H7. A local 3D map is reconstructed using visual odometry at about 10 Hz and the footstep planner replans at intervals of four steps. The robot walked across a floor, avoiding obstacles and reaching the goal.

Running is a complex dynamical task which places strict design requirements on both the physical components and software control systems of a robot. This paper explores some of those requirements and illustrates how a variable compliance actuation system can satisfy them. We present the design, analysis, simulation, and benchtop experimental validation of such an actuator system. We demonstrate, through simulation, the application of our prototype actuator to the problem of biped running.