Robots Leap and Crawl Over Obstacles With New Mapping Model
Robots Leap and Crawl Over Obstacles With New Mapping Model
5 months ago
ieee.org
4.5K views · 76 reactions | Learning Robust Autonomous Navigation and...
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Learning Locomotion over Challenging Terrain
Learning Locomotion over Challenging Terrain
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ethz.ch
Reinforcement learning has proven to aid locomotion control of highly articulated robotic systems. 🎥 Robotic Systems Lab: Legged Robotics at ETH Zürich | Interesting Engineering
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Reinforcement learning has proven to aid locomotion control of highl…
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Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either develop conservative controllers (
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FacebookWevolver.com
Multi-robot motion coordination is a fundamental robotics problem with wide-ranging applications that range from urban search and rescue to the control of fleets of self-driving cars to formation-flying in cluttered environments. ⁠ ⁠ Two key challenges make multi-robot coordination difficult: first, robots moving in new environments must make split-second decisions about their trajectories despite having incomplete data about their future path; second, the presence of larger numbers of robots in
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