PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball
Researchers introduce PAC-MAN, a framework for safe humanoid dodgeball using perception-aware control-barrier function reinforcement learning. The system enables robots to dodge balls using real-time sensing and adversarial motion prior.
- PAC-MAN is a perception-aware CBF-RL framework for whole-body safety in humanoid dodgeball
- The system uses real-time sensing from a head-mounted camera to detect the ball
- An adversarial motion prior is used to regularize the resulting evasive reflexes
- The framework is evaluated on a controlled benchmark with seeded throws
The PAC-MAN framework is designed to ensure whole-body safety in humanoid dodgeball. It uses a head-mounted camera to detect the ball and calculate clearance to every body link.
The system is trained using control-barrier function reinforcement learning, which couples control-barrier safety with deployment-realistic onboard sensing. An adversarial motion prior is used to regularize the resulting evasive reflexes.
The framework is evaluated on a controlled benchmark with seeded throws in two regimes: single throws and a deployment loop. In the deployment loop, the robot walks back to its station and recovers after each throw.
The PAC-MAN framework has potential applications in robotics and AI, particularly in areas where safety and real-time sensing are critical.
Advances safety in robotics and AI
- CBF-RL
- Control-barrier function reinforcement learning
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