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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.10635 |
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| _version_ | 1866914157190709248 |
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| author | Strauch, Pascal Müller, David Christen, Sammy Serifi, Agon Grandia, Ruben Knoop, Espen Bächer, Moritz |
| author_facet | Strauch, Pascal Müller, David Christen, Sammy Serifi, Agon Grandia, Ruben Knoop, Espen Bächer, Moritz |
| contents | Despite recent advances in robust locomotion, bipedal robots operating in the real world remain at risk of falling. While most research focuses on preventing such events, we instead concentrate on the phenomenon of falling itself. Specifically, we aim to reduce physical damage to the robot while providing users with control over a robot's end pose. To this end, we propose a robot agnostic reward function that balances the achievement of a desired end pose with impact minimization and the protection of critical robot parts during reinforcement learning. To make the policy robust to a broad range of initial falling conditions and to enable the specification of an arbitrary and unseen end pose at inference time, we introduce a simulation-based sampling strategy of initial and end poses. Through simulated and real-world experiments, our work demonstrates that even bipedal robots can perform controlled, soft falls. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robot Crash Course: Learning Soft and Stylized Falling Strauch, Pascal Müller, David Christen, Sammy Serifi, Agon Grandia, Ruben Knoop, Espen Bächer, Moritz Robotics Machine Learning Despite recent advances in robust locomotion, bipedal robots operating in the real world remain at risk of falling. While most research focuses on preventing such events, we instead concentrate on the phenomenon of falling itself. Specifically, we aim to reduce physical damage to the robot while providing users with control over a robot's end pose. To this end, we propose a robot agnostic reward function that balances the achievement of a desired end pose with impact minimization and the protection of critical robot parts during reinforcement learning. To make the policy robust to a broad range of initial falling conditions and to enable the specification of an arbitrary and unseen end pose at inference time, we introduce a simulation-based sampling strategy of initial and end poses. Through simulated and real-world experiments, our work demonstrates that even bipedal robots can perform controlled, soft falls. |
| title | Robot Crash Course: Learning Soft and Stylized Falling |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2511.10635 |