Collision Prediction for Robotics Accelerators

Motion planning in dynamic environments is an important task for autonomous robotics. Emerging approaches employ neural networks that can learn by observing (e.g., human) experts. Such motion planners react to the environment by continually proposing candidate paths to reach a goal. Some of these candidate paths may be unsafe-i.e., cause collisions. Hence, proposed paths must be checked for safety using collision detection. We observe that $25 \%-41 \%$ of the resulting collision detection queries can be eliminated if we can anticipate which queries will return an unsafe result. We leverage this observation to propose a mechanism, COORD, to predict whether a given robot position (pose) along a proposed path will result in a collision. By prioritizing the detailed evaluation of predicted collisions, COORD enables quickly eliminating invalid paths proposed by neural network and other sampling based motion planners. COORD does this by exploiting the physical spatial locality of different robot poses and using simple hashing and saturating counters. We demonstrate the potential of collision prediction on different computation platforms, including CPU, GPU, and ASIC. We further propose a hardware collision prediction unit (COPU), and integrate it with an existing collision detection accelerator. This results in an average $17.2 \%-32.1 \%$ decrease in number of collision detection queries across different motion planning algorithms and robots. When applied to a state-of-the-art neural motion planner [41], COORD improves performance/watt by $1.23 \times$ on average for motion planning queries of varying difficulty levels. Further, we find that the benefits of collision prediction grow as the compute complexity of motion planning queries increases and provides $1.30 \times \mathrm{im}-$ provement in performance/watt in narrow passages and cluttered environments.