Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance
Collision-free motion is often aided by tactile and proximity sensors distributed on the body of the robot due to their resistance to occlusion as opposed to external cameras. However, how to shape the sensor's properties, such as sensing coverage; type; and range, to enable avoidant behavior remains unclear. In this work, we present a reinforcement learning framework for whole-body collision avoidance on a humanoid H1-2 robot and use it to characterize how sensor properties shape learned avoidance behavior. Using dodgeball as a benchmark task, we ablate the properties of sensors distributed across the upper body of the robot and find that raw proximity measurements can substitute for exp...
| Authors | Carson Kohlbrenner, Niraj Pudasaini, William Xie et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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