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Found 3 papers, 1 papers with code
Touch-based Curiosity for Sparse-Reward Tasks
Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion.
Probabilistic Planning with Sequential Monte Carlo methods
In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories.
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states.
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