Practical 3-D object detection using category and instance-level appearance models

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.

Authors

• Kate Saenko
• Sergey Karayev
• Yangqing Jia
• Alex Shyr
• Allison Janoch
• Jonathan Long
• Mario Fritz
• Trevor Darrell

Keywords

• Legged locomotion
• Foot
• Torso
• Biological system modeling
• Humanoid robots
• Robot sensing systems
• Uneven Terrain
• Surface Learning
• Control Of Walking
• Bipedal Walking
• Push Recovery
• Learning Algorithms
• Error Model
• Surface Model
• Uneven Surface
• External Disturbances
• Reinforcement Learning Algorithm
• Learning Control
• Hierarchical Control
• Robot Model
• Humanoid Robot
• Onboard Sensors
• Specific Hardware
• Walking Environment
• Small Robot
• Online Learning Algorithm
• Inertial Measurement Unit
• Landing Position
• Physical Robot
• Local Height
• High Level Of Control
• Simulation Environment
• Low-level Control
• Proprioceptive
• Center Of Mass
• Walking Conditions
• Surface Model Learning
• Full Body Push Recovery
• Reinforcement Learning