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DribbleBot: Dynamic Legged Manipulation in the Wild
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DribbleBot: Dynamic Legged Manipulation in the Wild
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DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged robotic system that can dribble a soccer ball under the same real-world conditions as humans (i.e., in-the-wild). We adopt the paradigm of training policies in simulation using reinforcement learning and transferring them into the real world. We overcome critical challenges of accounting for variable ball motion dynamics on different terrains and perceiving the ball using body-mounted cameras under the constraints of onboard computing. Our results provide evidence that current quadruped platforms are well-suited for studying dynamic whole-body control problems involving simultaneous locomotion and manipulation directly from sensory observations.
Forward citations
Cited by 4 Pith papers
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SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision
SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active s...
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HeLoM: Hierarchical Learning for Whole-Body Loco-Manipulation by a Hexapod Robot
A hexapod pushes boxes with unknown mass, size, and friction to target poses by coordinating front-leg contact with hind-leg balance via a hierarchical learned controller.
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MuGen: Multi-Skill Generative Locomotion Controller for Humanoid Robots
MuGen learns a generative latent representation of multi-skill humanoid locomotion from heterogeneous human data using VQ-VAEs and RL, then distills a deployable policy that tracks unseen motions and reuses the latent space.
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Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.
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