Humanoid Motion Planning Research Articles

Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning

The use and application of robotic arms in helping the aged and vulnerable persons are increasing gradually. In order to achieve safer and reliable human-robot interaction and its wider adoption, the requirements for the humanoid motion of robotic arms are becoming more stringent. This paper presents a humanoid motion planning method for a robotic arm based on the physics of human arm and reinforcement learning. Firstly, the humanoid motion rules are extracted by analyzing and learning the action data of human arm, which is collected using the VICON optical motion capture system. Then, according to the acquired features and rules, the corresponding reward functions are proposed and the humanoid motion training of the robotic arm is carried out by using the reinforcement learning based on Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) algorithm. Finally, the experiments are carried out to verify whether the robotic arm motions planned by the proposed approach are humanoid, and the observed results show its feasibility and effectiveness in planning the humanoid motion of the robotic arm.

Supervoxel Plane Segmentation and Multi-Contact Motion Generation for Humanoid Stair Climbing

Stair climbing is still a challenging task for humanoid robots, especially in unknown environments. In this paper, we address this problem from perception to execution. Our first contribution is a real-time plane-segment estimation method using Lidar data without prior models of the staircase. We then integrate this solution with humanoid motion planning. Our second contribution is a stair-climbing motion generator where estimated plane segments are used to compute footholds and stability polygons. We evaluate our method on various staircases. We also demonstrate the feasibility of the generated trajectories in a real-life experiment with the humanoid robot HRP-4.

A humanoid robotics simulation and control platform for NINO

The recent increase in technological maturity has allowed humanoid robots to interact with humans in real-world applications. A simulator of humanoid robots with the characteristics of digital human models plays a significant role for humanoid motion planning in those complex environments. In this paper, a humanoid robotics simulation and control platform is proposed for the developed humanoid robot, NINO. The platform consists of a simulator, the visualisation tool, motion planning algorithms, and a motion control library. It can simulate realistic physical interactions between the robot and its environment during locomotion and keep consistent performance between the simulator and the actual humanoid robot. The simulator can control both the virtual and physical robots. The performance and applications of the simulator platform have been justified through the simulation results of several biped locomotion tasks.

An Evolutionary Computational Approach to Humanoid Motion Planning

The theme of our work is centred on humanoid motion planning and balancing using evolutionary computational techniques. Evolutionary techniques, inspired by the Darwinian evolution of biological systems, make use of the concept of the iterative progress of a population of solutions with the aim of finding an optimally fit solution to a given problem. The problem we address here is that of asymmetric motion generation for humanoids, with the aim of automatically developing a series of motions to resemble certain predefined postures. An acceptable trajectory and stability is of the utmost concern in our work. In developing these motions, we are utilizing genetic algorithms coupled with heuristic knowledge of the problem domain. Unlike other types of robots, humanoids are complex in both construction and operation due to their myriad degrees of freedom and the difficulty of balancing on one or more limbs. The work presented in this paper includes the adopted methodology, experimental setup, results and an analysis of the outcome of a series of evolutionary experiments conducted for generating the said asymmetric motions.

State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.