Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading to suboptimal motion and even collisions. To address these issues, we introduce ZeroGrasp, a novel framework that simultaneously performs 3D reconstruction and grasp pose prediction in near real-time. A key insight of our method is that occlusion reasoning and modeling the spatial relationships between objects is beneficial for both accurate reconstruction and grasping. We couple our method with a novel large-scale synthetic dataset, which comprises 1M photo-realistic images, high-resolution 3D reconstructions and 11.3B physically-valid grasp pose annotations for 12K objects from the Objaverse-LVIS dataset. We evaluate Zero-Grasp on the GraspNet-1B benchmark as well as through real-world robot experiments. ZeroGrasp achieves state-of-the-art performance and generalizes to novel real-world objects by leveraging synthetic data. https://sh8.io/#/zerograsp

Grasp detection methods typically target the detection of a set of free-floating hand poses that can grasp the object. However, not all of the detected grasp poses are executable due to physical constraints. Even though it is straightforward to filter invalid grasp poses in the post-process, such a two-staged approach is computationally inefficient, especially when the constraint is hard. In this work, we propose an approach to take the following two constraints into account during the grasp detection stage, namely, (i) the picked object must be able to be placed with a predefined configuration without in-hand manipulation (ii) it must be reachable by the robot under the joint limit and collision-avoidance constraints for both pick and place cases. Our key idea is to train an SE(3) grasp diffusion network to estimate the noise in the form of spatial velocity, and constrain the denoising process by a multi-target differential inverse kinematics with an inequality constraint, so that the states are guaranteed to be reachable and placement can be performed without collision. In addition to an improved success ratio, we experimentally confirmed that our approach is more efficient and consistent in computation time compared to a naive two-stage approach.

This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects and when attempting to generalize to new environments solely through synthetic data training. In this work, we address these challenges by proposing a probabilistic model that relies on diffusion to estimate dense canonical maps crucial for recovering partial object shapes as well as establishing correspondences essential for pose estimation. Furthermore, we introduce critical components to enhance performance by leveraging the strength of the diffusion models with multi-modal input representations. We demonstrate the effectiveness of our method by testing it on a range of real datasets. Despite being trained solely on our generated synthetic data, our approach achieves state-of-the-art performance and unprecedented generalization qualities, outperforming baselines, even those specifically trained on the target domain. Our code and data for the generalization benchmark can be found at https://woven-planet.github.io/DiffusionNOCS/.

Learning-based grasp detectors typically assume a precision grasp, where each finger only has one contact point, and estimate the grasp probability. In this work, we propose a data generation and learning pipeline that can leverage power grasping, which has more contact points with an enveloping configuration and is robust against both positioning error and force disturbance. To train a grasp detector to prioritize power grasping while still keeping precision grasping as the secondary choice, we propose to train the network against the magnitude of disturbance in the gravity direction a grasp can resist (gravity-rejection score) rather than the binary classification of success. We also provide an efficient data generation pipeline for a dataset with gravity-rejection score annotation. Evaluation in both simulation and real-robot clarifies the significant improvement in our approach, especially when the objects are heavy.

In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation, it cannot represent the ambiguity of the observation. In order to handle the ambiguity, the Bingham distribution is one promising solution. However, it requires complicated calculation when yielding the negative log-likelihood (NLL) loss. An alternative easy-to-implement loss function has been proposed to avoid complex computations but has difficulty expressing symmetric distribution. In this paper, we introduce a fast-computable and easy-to-implement NLL loss function for Bingham distribution. We also create the inference network and show that our loss function can capture the symmetric property of target objects from their point clouds.

In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is detected by a simple but robust vein line detector. Thanks to the proposed two-staged calibration process, the total time for the injection process is limited to 1.5 minutes, despite that the position of needle and tail vein varies for each trial. We performed an injection experiment targeting 40 mice and succeeded to inject saline to 37 of them, resulting 92.5% success ratio.

In recent years, synthetic data has been widely used in the training of 6D pose estimation networks, in part because it automatically provides perfect annotation at low cost. However, there are still non-trivial domain gaps, such as differences in textures/materials, between synthetic and real data. These gaps have a measurable impact on performance. To solve this problem, we introduce a simulation to reality (sim2real) instance-level style transfer for 6D pose estimation network training. Our approach transfers the style of target objects individually, from synthetic to real, without human intervention. This improves the quality of synthetic data for training pose estimation networks. We also propose a complete pipeline from data collection to the training of a pose estimation network and conduct extensive evaluation on a real-world robotic platform. Our evaluation shows significant improvement achieved by our method in both pose estimation performance and the realism of images adapted by the style transfer.

Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent of footstep planning. Thus, we propose that robots such as humanoid robots that have manipulation capabilities should use them. A robot should autonomously modify its environment if necessary. We present a system that enabled a real robot to use a box to create itself a stair step or place a board on the ground to cross a gap, allowing it to reach its otherwise unreachable goal configuration.

The present paper proposes a walking control of a humanoid that uses heel and toe edge landing for better adaptability to rough terrain. We focus on the control of forward straight walking and the roughness, such as changes in level and inclination, along the walking direction is explicitly considered herein. The requirements for designing the walking parameters, such as the step length and the step cycle for continuous walking, are discussed based on assumptions on the roughness of the terrain. An online adaptation strategy that includes change in stepping length, step cycle, and landing edge is discussed. The adaptation strategy uses the actual center of mass motion and the contact status to the ground for changing the stepping length, the step cycle, and the landing edge. We extended a walking pattern generation method that can handle the permissible region of the ZMP for balance recovery to a version that can also simultaneously handle changes in stepping position if needed. This method is used in the abovementioned discussions. A compensation method for the effect of the multibody system is also presented, so that these discussions can be made applicable to the actual humanoid system.

A walking biped robot is required to change its walking direction and speed with minimal delay and not to tip off even when subjected to an unexpected external force. A major drawback of Zero Moment Point (ZMP) based online walking pattern generation methods is that arbitrary ZMPs cannot be achieved without divergence of the Conter of Mass (CoM). In this paper, we propose a new online walking trajectory generation method that utilizes nondivergence conditions of ZMP-CoM in a successive manner. This method enables changes in the walking direction and speed of a robot and push recovery under an unknown external force in unifie form. Experiments confir walking change of up to 4.05 km/h in the speed and changes in the walking direction with minimum delay and successful push recovery under an implse of 22 Ns.

This paper presents an online generation method of a torso height trajectory as a part of walking pattern generation. Torso height trajectories should be designed carefully in order to meet the constraints that are given by the existence of the inverse kinematics solution and limitations of the leg joint angle velocity. Limitations of the height in the future are evaluated and taken into consideration for deciding the height of the torso at a moment. In this manner a trajectory that satisfies the limitations and given maximum acceleration and velocity is generated. The proposed method is implemented as a part of online walking control system of full-size Humanoid HRP-2. Its performance is confirmed through the pattern generation in the walking control system.

The present paper addresses strategies of changing the reference trajectories of the future ZMP that are used for online repetitive walking pattern generation. Walking pattern generation operates with a cycle of 20 [ms], and the reference ZMP trajectory is adjusted according to the current actual motion status in order to maintain the current balance. Three different strategies are considered for adjusting the ZMP. The first strategy is to change the reference ZMP inside the sole area. The second strategy is to change the position of the next step, and the third strategy is to change the duration of the current step. The manner in which these changes affect the current balance and how to combine the three strategies are discussed. The proposed methods are implemented as part of an online walking control system with short cycle pattern generation and are evaluated using the HRP-2 full-sized humanoid robot.

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 video demonstrates a mixed reality (MR) environment which is constructed for development of autonomous behaviors of robots. Many kinds of functions are required to be integrated for realizing an autonomous behavior. For example, autonomous navigation of humanoid robots needs functions, such as, recognition of environment, localization and mapping, path planning, gait planning, dynamically stable biped walking pattern generation, and sensor feedback stabilization of walking. Technologies to realize each function are well investigated by many research works. However, another effort is required for constructing an autonomous behavior by integrating those functions. We demonstrate a MR environment in which internal status of a robot, such as, sensor status, recognition results, planning results, and motion control parameters, can be projected to the environment and its body. We can understand intuitively how each function works as a part of total system in the real environment by using the proposed system, and it helps solving the integration problems. The overview of the system, projection of each internal status, and the application to an autonomous locomotion experiment are presented in the video clip.

We present results of successful telemanipulation of large, heavy objects by a humanoid robot. Using a single joystick the operator controls walking and whole body manipulation along arbitrary paths for up to ten minutes of continuous execution. The robot grasps, walks, pushes, pulls, turns and re-grasps a 55kg range of loads on casters. Our telemanipulation framework changes reference frames online to let the operator steer the robot in free walking, its hands in grasping and the object during mobile manipulation. In the case of manipulation, our system computes a robot motion that satisfies the commanded object path as well as the kinematic and dynamic constraints of the robot. Furthermore, we achieve increased robot stability by learning dynamic friction models of manipulated objects.

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.

A sensor feedback framework that realizes robust walking of humanoids are discussed in the present paper. We have proposed an online walking control system that generates a dynamically stable motion pattern in short cycles, such as 40 [ms]. The system is capable of reflecting the actual motion status modified by sensor feedback to the pattern generation of the next period so that the long-term stability of the walking is maintained using the dynamic model. We herein propose three categories of adaptation as a framework for realizing robust walking via the short-cycle generation system: 1) absorption of the error of the dynamic model, 2) reactive adaptation to the disturbance, and 3) adjustment of the parameters that are used to generate the walking pattern. Feedback methods for each category are discussed and validated on the full-size humanoid HRP-2.

One of the problems in developing a humanoid robot is caused by the fact that intermediate results, such as what the robot perceives the environment, and how it plans its moving path are hard to be observed online in the physical environment. What developers can see is only the behavior. Therefore, they usually investigate logged data afterwards, to analyze how well each component worked, or which component was wrong in the total system. In this paper, we present a novel environment for robot development, in which intermediate results of the system are overlaid on physical space using mixed reality technology. Real-time observation enables the developers to see intuitively, in what situation the specific intermediate results are generated, and to understand how results of a component affected the total system. This feature makes the development efficient and precise. This environment also gives a human-robot interface that shows the robot internal state intuitively, not only in development, but also in operation.

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

In this paper, we present an algorithm for manipulability based trajectory generation for any serial manipulator that has an inverse kinematic model that can obtain all solutions. Our strategy is a search based approach that analyzes candidate configurations at discrete points along the work-space trajectory. Given such a model we prove the configuration-space trajectories generated are optimal within the limit of the discretization of the work-space trajectory

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

This paper addresses a dynamically stable humanoid walking pattern generation system that can update the pattern at a short cycle such as 40 [ms]. Frequent update of the pattern enables a robot to reflect change of moving direction and upper body posture with short delay after the change of the command input. It also enables a robot to change the walking pattern according to the change of measurable applied force. Moreover short cycle generation is useful in constructing a system that reflects motion status modified by sensor feedback to the motion generation layer in order to maintain consistency of dynamics. Preview control theory is adopted to decide horizontal torso trajectory that realizes desired ZMP trajectory. The proposed system is implemented on a full-size humanoid HRP-2. Walking pattern is updated at every 40 [ms] with reflecting the current modified torso position in the system. Walking control is successfully carried out by the implemented system. Then an experiment of generating and executing walking pattern while variable force is applied to the hand is shown. The system generates walking pattern reflecting the force sensor information at the wrist while walking

This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain of navigation among movable obstacles (NAMO). The robot perceives and constructs a model of an environment filled with various fixed and movable obstacles, and automatically plans a navigation strategy to reach a desired goal location. The planned strategy consists of a sequence of walking and compliant manipulation operations. It is executed by the robot with online feedback. We give an overview of our NAMO system, as well as provide details of the autonomous planning, online grasping and compliant hand positioning during dynamically-stable walking. Finally, we present results of a successful implementation running on the humanoid robot HRP-2

In this paper, we explore preprocessing techniques aimed at efficiently mapping the workspace to the configuration space for redundant manipulators. Exploiting the increasing availability of memory we precompute a database for online use that allows fast querying of joint configurations with high manipulability. Such a system is useful for performing reaching and manipulation tasks, online force control, and quickly computing goal configurations for path planning algorithms. We have implemented a prototype system on the digital human version humanoid robot platform 2 (DH-HRP2) which we use to efficiently compute arm inverse kinematic solutions. We demonstrate the benefits of such a system applied to converting workspace trajectories to configuration space trajectories. Although we have applied the technique to finding joint configurations with high manipulability, our approach can be generalized to a variety of different priority measures that may be used for evaluating inverse kinematic solutions for redundant manipulators.

This paper describes an online 3D reconstruction system from stereo image sequences to obtain a dense local world model for robot navigation. The proposed method consists of three components: 1) stereo depth map calculation, 2) correspondence calculation in time sequential images by tracking raw image features, 3) 6DOF camera motion estimation by RANSAC and integrate depth map into 3D reconstructed model. We examined and evaluated our method in a motion capture environment for comparison. Finally experimental results of a humanoid robot H7 are denoted.

This paper describes a 32 /spl times/ 32 matrix scan type high-speed pressure sensor for the feet of humanoid robots that has 1 kHz sampling rate. This sensor has matrix scan circuit. The matrix scan method has a problem of interference by bypass current. To resolve this problem, we suggest a novel method using a very thin conductive rubber. We adopted a very thin (0.6 mm) force sensing conductive rubber sheet for high speed sensing. Each sensing area is 4.2 /spl times/ 7.0 mm and can measure vertical force of approximately 0.25-20 N. Walking cycle of humanoid robot as well as human being is about 0.4-0.8 s and dual leg phase is about 0.1-0.15 s. The target of the sensor is biped walk stabilization so that high-speed input is important. Matrix scan type circuit is connected to sensor, and the system runs 1 kHz with 14 bit resolution at 4.2 /spl times/ 7.0 mm grid for 32 /spl times/ 32 points, and the sensor size is the same as humanoid robot foot 135 /spl times/ 228 mm, The system is running high-speed because of the very thin conductive rubber and simultaneous measurement. The sensor system, a novel scan method, and evaluation results are described.

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.

In this paper we present "hand-centered" motion generation and control of whole-body motion for a humanoid robot, which enable the robot to carry out reaching and walking simultaneously. Hand-centered whole-body motion control is defined as a real-time control scheme in which the hand motion (input) leads to the whole-body motion (output). The hand trajectory is described in the absolute coordinate system (world frame) and the torso trajectory follows the hand trajectory. This method enables operation of the hand of a walking humanoid robot, and autonomous generation and control of the whole-body motion based on the hand velocity vector using sensor feedback. We have implemented the method as a hand-centered whole-body motion control system on the existing humanoid robot system "H7". Some experimental results including both reaching and walking motions are shown to demonstrate the effectiveness of the proposed method.

We present an integrated humanoid locomotion and online terrain modeling system using stereo vision. From a 3D depth map, a 2.5D probabilistic description of the nearby terrain is generated. The depth map is calculated from a pair of stereo camera images, correlation-based localization is performed, and candidate planar walking surfaces are extracted. The results are used to update a probabilistic map of the terrain, which is input to an online footstep planning system. Experimental results are shown using the humanoid robot H7, which was designed as a research platform for intelligent humanoid robotics.

We present an online algorithm for planning sequences of footstep locations that encode goal-directed navigation strategies for humanoid robots. Planning footsteps is more general than most existing navigation methods designed for wheeled robots, since the options of stepping over or upon obstacles in a cluttered terrain are available. Given a discrete set of plausible footstep locations, a forward dynamic programming approach is used to compute a footstep sequence to a specified goal location in the environment. Heuristics designed to minimize the number and complexity of the step motions are used to encode cost functions used for searching a footstep transition graph. If successful, the planner returns an optimal sequence of footstep locations according to the cost functions and plausible sets of footstep locations defined. We show results from an experimental implementation of the algorithm running on the H7 humanoid robot. Using a stereo vision system to sense obstacles in the immediate environment and identify a target goal location, the robot updates the current optimal footstep sequence to the goal from its present location.

We present a humanoid walking control system that generates body trajectories to follow a given desired motion online. A layered software and control architecture is used to aggregate system components and provide a framework for high-level autonomous locomotion behaviors. Walking characteristics such as desired torso movements, upper body posture and step cycles, can be specified and used to generate stable whole-body walking trajectories online. The basic architecture consists of 4 layers: footstep planning, trajectory generation, trajectory modification from sensor feedback, and joint servo control. In order to test the complete system, we implemented an experimental top-level behavior that enables the robot to track and follow a moving target based on stereo vision feedback. We explain the overall system design and components, and present results based on our current implementation using the humanoid robot 'H7'.

This paper describes an experimental stereo vision based motion planning system for humanoid robots. The goal is to automatically generate arm trajectories that avoid obstacles in unknown environments from high-level task commands. Our system consists of three components: 1) environment sensing using stereo vision with disparity map generation and on-line consistency checking, 2) probabilistic mesh modeling in order to accumulate continuous vision input, and 3) motion planning for the robot arm using RRTs (rapidly exploring random trees). We demonstrate results from experiments using an implementation designed for the humanoid robot H7.

We present an approach to self-collision detection suitable for complex articulated robots such as humanoids. Preventing self-collisions is vital for the safe operation of robots that generate body trajectories online. Our approach uses a fast distance determination method for convex polyhedra in order to conservatively guarantee that a given trajectory is free of self-collision. Experimental results using an online joystick control application for the humanoid robot "H7" demonstrate the feasibility and effectiveness of the method.

Addresses the extension of a humanoid's action capability by attaching toe joints. The effectiveness of toe joints is discussed in three aspects. One is utilizing it to speed up the walking, another is using it to enable a humanoid to go up higher steps, and the other is using it to whole-body action in which knees are contacting the ground. Feet with toe joints are developed for humanoid 'H6'. An experiment of the wholebody action in which knees are contacting the ground is carried out to show the usefulness of toe joints for such actions. Then the walking pattern generation system is extended to use toe joints. Using this extended system maximum speed of knee joints can be reduced at the same walking speed, and 80% faster walking speed is achieved on humanoid 'H6'.

This paper describes a design of six-axis force sensor that measures ground reaction force of human or humanoid robot. The key concept is parallel support mechanisms, that allow large torques and forces which are caused when foot is hitting to the environment. Basic concept and design of parallel support mechanisms are denoted. Finally ground reaction force measurement system for human walking, and application to humanoid robot walking are described.

This paper describes the design of an integrated system for humanoid robotics that consists of three key components, 3D vision, motion planning and bipedal control. Layered system design is adopted to achieve concurrency as well as small latency. Implementation using our humanoid type robot H7 and experiments with this architecture are described. The H7 is expected to be a common test-bed in experiments and discussion for various aspects of intelligent humanoid robotics.

This paper presents an efficient online method to generate humanoid walking motions that satisfy desired upper body trajectories while simultaneously carrying objects. A fast motion pattern generation technique that follows the desired ZMP is adopted. In order to satisfy the control input given online, subsequent motion patterns are updated and connected in a stable manner to the old ones while executing. During the creation of motion trajectories online, the commanded motion parameters are checked and modified automatically considering the performance limitations of the hardware. As an example application, we have implemented a one step cycle control system on the Humanoid H7. Experiments controlling the upper body motion and walking direction using a joystick interface are explained to demonstrate the validity of the proposed method.

This paper describes a design of remote operation interface for humanoid robot with following three functions: (1) Body DOFs control interface through 3D robot model in a virtual environment, and it has two types of online stabilizing software for maintaining the body balance. (2) Environmental recognition display interface for sensors, sound and 3D vision. (3) Voice and face recognition interface for interacting with operator and a human in front of the robot body. Then, implementation and experiments on our humanoid robot H6 are denoted.

The H6 humanoid robot has been developed as a platform for the research on perception-action coupling in intelligent behaviour of humanoid type robots. The H6 features: 1) a body which has enough DOFs and each joint has enough torque for full body motion; 2) a PC/AT compatible high-performance on-board computer which is controlled by RT-Linux so that from low-level to high-level control is achieved simultaneously; 3) self-contained and connected to a network via radio Ethernet; and 4) dynamic walking trajectory generation, motion planning and 3D vision functions are available. The H6 can be used as a common test-bed in experiment for various aspects of intelligent humanoid robotics.

We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using the humanoid robot "H6".

This paper introduces and describes a novel method which enables the online generation of humanoid walking patterns that follow desired footprint locations. Online generation is realized by the dynamically stable mixture and connection of pre-designed motions. Characteristics of zero moment point (ZMP) are utilized in order to maintain the overall dynamic stability of "mixed" motions. Experiments using an online pattern generation software system and online walking control joystick interface for the humanoid robot H6 are introduced to show the validity of the method.

We present an algorithm for planning safe navigation strategies for biped robots moving in obstacle-cluttered environments. From a discrete set of plausible statically-stable, single-step motions, a forward dynamic programming approach is used to compute a sequence of feasible footstep locations. In contrast to existing navigation strategies for mobile robots, our method is a global method that takes into account the unique ability of legged robots such as bipedal humanoids to traverse obstacles by stepping over them. Heuristics designed to minimize the number and complexity of the step motions are used to encode cost functions used for searching a footstep transition graph. We show preliminary results of an experimental implementation of the algorithm using a model of the H6 humanoid navigating on an office floor littered with obstacles.

A humanoid robot "H6" is developed as a platform for the research on perception-action coupling in intelligent behaviour of humanoid type robots. The H6 has the features as follows: 1) a body which has enough DOFs and each joint has enough torque for full body motion, 2) a PC/AT compatible on-board computer which is controlled by RT-linux so that low-level to high-level control is achieved simultaneously, 3) is self-contained and connected to a network via radio ethernet, 4) a 3D vision function can be applied. The H6 is expected to be a common test-bed for experiment and discussion for various aspects of intelligent humanoid robotics.

This article addresses the development of a light-weight, human-size and low-cost developing humanoid robot named Saika. Saika has a 2-DOF neck, two 5-DOF upper arms, a torso and a head. Several types of hands and forearms are developed. They are chosen depending upon the tasks to perform. The features of Saika are: (a) Saika as modularized to reduce the developing cost and to make maintenance easy, (b) the total weight of the head, the neck, the two upper arms and the torso is only eight kilograms and (c) most of the motors are installed inside the arms and the torso.