MSDP pretrains a transformer encoder via masked multisensory reconstruction and feeds the embeddings into an asymmetric actor-critic RL setup, yielding faster learning and high real-robot success rates with only 6,000 interactions.
Partially observable markov deci- sion processes (pomdps) and robotics
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UniVLA trains cross-embodiment vision-language-action policies from unlabeled videos via a latent action model in DINO space, beating OpenVLA on benchmarks with 1/20th pretraining compute and 1/10th downstream data.
The paper proposes a unified risk map modeling and learning framework integrated with diffusion-based adversarial scenario generation for risk-aware planning in partially observable autonomous driving, demonstrating improved time-to-collision metrics on the Waymo Open Motion Dataset.
citing papers explorer
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Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
MSDP pretrains a transformer encoder via masked multisensory reconstruction and feeds the embeddings into an asymmetric actor-critic RL setup, yielding faster learning and high real-robot success rates with only 6,000 interactions.
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UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
UniVLA trains cross-embodiment vision-language-action policies from unlabeled videos via a latent action model in DINO space, beating OpenVLA on benchmarks with 1/20th pretraining compute and 1/10th downstream data.
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Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
The paper proposes a unified risk map modeling and learning framework integrated with diffusion-based adversarial scenario generation for risk-aware planning in partially observable autonomous driving, demonstrating improved time-to-collision metrics on the Waymo Open Motion Dataset.