PointWalker: Mapless Quadruped Locomotion Directly From Raw
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: PointWalker: Mapless Quadruped Locomotion Directly From Raw LiDAR Point Clouds. This paper presents PointWalker, a mapless framework for quadruped locomotion that directly processes raw LiDAR point clouds without terrain maps or foothold estimation. Using reinforcement learning with terrain-aware re…
Generated from attributed reports · Updated 49 minutes ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
Reported quantity · gap width: 0.8 other · Basis not reported
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
Reported quantity · platform height: 0.8 other · Basis not reported
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
Reported quantity · slope angle: 45 other · Basis not reported
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
Reported quantity · payload weight: 50 other · Basis not reported
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
Reported quantity · control frequency: 50 other · Basis not reported
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
Reported quantity · terrain classes: 7 other · Basis not reported
“n geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45{\deg} steel-grating st”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsPointWalker: Mapless Quadruped Locomotion Directly From Raw LiDAR Point Clouds
This paper presents PointWalker, a mapless framework for quadruped locomotion that directly processes raw LiDAR point clouds without terrain maps or foothold estimation. Using reinforcement learning with terrain-aware rewards and dual-channel simulation, the method achieves a 90.1% mean success rate in simulation and successfully navigates complex terrains on a Deep Robotics X30 robot.
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