A liveness-based Bellman operator enables conservative offline policy evaluation for manipulation tasks by encoding task progression and reducing truncation bias from finite horizons.
Dex- terous manipulation with deep reinforcement learning: Efficient, gen- eral, and low-cost, in: 2019 International Conference on Robotics and Automation (ICRA), pp
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A conformal prediction certification for belief-space safety filters focuses verification on reliable inference regions to produce less conservative yet high-probability safe filters than standard baselines in human-vehicle simulations.
A VAE-based latent task representation enables automatic curriculum generation in CRL for non-Euclidean navigation tasks, outperforming interpolation and GAN-based methods in experiments.
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Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation
A liveness-based Bellman operator enables conservative offline policy evaluation for manipulation tasks by encoding task progression and reducing truncation bias from finite horizons.
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Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
A conformal prediction certification for belief-space safety filters focuses verification on reliable inference regions to produce less conservative yet high-probability safe filters than standard baselines in human-vehicle simulations.
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Curriculum reinforcement learning with measurable task representation learning
A VAE-based latent task representation enables automatic curriculum generation in CRL for non-Euclidean navigation tasks, outperforming interpolation and GAN-based methods in experiments.