Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators

Ma, Yuntao; Farshidian, Farbod; Hutter, Marco

doi:10.48550/arxiv.2303.05486

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…In addition, there is a growing interest in whole-body control, that is, tasks in which the whole body is used in flexible ways to interact with the environment. Examples include getting up from the ground (55) and manipulation of objects with legs (23,56). Recently, RL has been applied to learn simple soccer skills, including goalkeeping (21), ball manipulation (18,19), and shooting (20).…”

Section: Discussionmentioning

confidence: 99%

Learning agile soccer skills for a bipedal robot with deep reinforcement learning

Haarnoja,

Moran,

Lever

et al. 2024

Sci. Robot.

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We investigated whether deep reinforcement learning (deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies. We used deep RL to train a humanoid robot to play a simplified one-versus-one soccer game. The resulting agent exhibits robust and dynamic movement skills, such as rapid fall recovery, walking, turning, and kicking, and it transitions between them in a smooth and efficient manner. It also learned to anticipate ball movements and block opponent shots. The agent’s tactical behavior adapts to specific game contexts in a way that would be impractical to manually design. Our agent was trained in simulation and transferred to real robots zero-shot. A combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training enabled good-quality transfer. In experiments, the agent walked 181% faster, turned 302% faster, took 63% less time to get up, and kicked a ball 34% faster than a scripted baseline.

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Section: Discussionmentioning

confidence: 99%

Learning agile soccer skills for a bipedal robot with deep reinforcement learning

Haarnoja,

Moran,

Lever

et al. 2024

Sci. Robot.

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“…falls (14,15), and dribbling with a football (16,17). In parallel, bipedal robots have also demonstrated their agile capabilities by walking blindly on rough terrain (18) and jumping on obstacles (19).…”

Section: Of 14mentioning

confidence: 99%

ANYmal parkour: Learning agile navigation for quadrupedal robots

Hoeller,

Rudin,

Sako

et al. 2024

Sci. Robot.

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Performing agile navigation with four-legged robots is a challenging task because of the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. Here, we propose a fully learned approach to training such robots and conquer scenarios that are reminiscent of parkour challenges. The method involves training advanced locomotion skills for several types of obstacles, such as walking, jumping, climbing, and crouching, and then using a high-level policy to select and control those skills across the terrain. Thanks to our hierarchical formulation, the navigation policy is aware of the capabilities of each skill, and it will adapt its behavior depending on the scenario at hand. In addition, a perception module was trained to reconstruct obstacles from highly occluded and noisy sensory data and endows the pipeline with scene understanding. Compared with previous attempts, our method can plan a path for challenging scenarios without expert demonstration, offline computation, a priori knowledge of the environment, or taking contacts explicitly into account. Although these modules were trained from simulated data only, our real-world experiments demonstrate successful transfer on hardware, where the robot navigated and crossed consecutive challenging obstacles with speeds of up to 2 meters per second.

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Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators

Cited by 2 publications

References 21 publications

Learning agile soccer skills for a bipedal robot with deep reinforcement learning

Learning agile soccer skills for a bipedal robot with deep reinforcement learning

ANYmal parkour: Learning agile navigation for quadrupedal robots

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