Robotics & embodied AI research

The frontier

Replay-free continual learning method prevents forgetting in vision-language-action models

The authors introduce SAMBAR, a continual learning algorithm for vision-language-action (VLA) models that requires no replay of previously seen demonstrations. SAMBAR frames continual learning as a constrained optimization problem, solved via the method of multipliers, and selectively anchors parameters important to earlier tasks. On the LIBERO benchmark and in hardware experiments, SAMBAR retained every learned task, while all replay-free baselines fully forgot the first task they were taught.

Aayushi Shrivastava, Xunlan Zhou, Hongrui Zhao +2

Robotics & embodied AI

Compressed world models make zero-shot driving plans far faster

Zhu and colleagues evaluate action-conditioned joint-embedding predictive architectures, or JEPAs, as driving world models without training a driving policy. Their proposed AD-E2E-JEPA compresses the latent patches used for planning and reports a 100-fold inference speedup, while retaining planning accuracy and improving representations transferred to imitation learning.

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Generative media

A method that generates consistent video observations across multiple vehicles in a shared driving scene

Meng et al. propose CoDrive, a cross-vehicle video generation framework that produces world-consistent observations for multiple vehicles in the same scene. By interleaving local and global self-attention and injecting camera trajectories in a shared coordinate system, the model significantly improves trajectory controllability and cross-agent geometric and instance consistency while maintaining visual quality.

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Robotics & embodied AI

A single world model enables robots to insert unseen parts zero-shot

Hansen et al. present InsertionWM, a framework that trains a single world model on up to 90 geometrically diverse insertion tasks using wrist-mounted camera images and robot proprioception. The model achieves 56% zero-shot success on unseen objects with unknown geometry, versus 7% for a model-free baseline. Finetuning the generalist model on held-out objects improves data-efficiency and asymptotic performance.

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Robotics & embodied AI

Canonical gripper-frame representation achieves zero-shot cross-embodiment transfer for two-finger manipulation

The authors propose an interaction-centric framework that uses a parameterized universal gripper abstraction to transform RGB-D observations into a canonical gripper frame. This enables zero-shot cross-embodiment and cross-viewpoint transfer for two-finger gripper manipulation tasks, demonstrated in both simulation and real-world experiments. The approach simultaneously achieves competitive benchmark performance and extreme generalization to heterogeneous robot platforms.

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Robotics & embodied AI

Trajectory optimization that preserves endpoints improves closed-loop driving

Zhang et al. introduce Endpoint-Constrained Optimization (ECO), a training-free post-processor for end-to-end driving models that fixes the vehicle's executed history and the policy's predicted endpoint, then optimises the intermediate waypoints for physical plausibility. Across two closed-loop simulators and five datasets, ECO improved the aggregate score of every evaluated policy, with gains up to 71% in HUGSIM score for the VaVAM model.

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Robotics & embodied AI

Aiming at observed intermediate targets beats final-goal scoring in frozen world model planning

The authors show that scoring predicted outcomes by their distance to the final goal can break latent world model planning even when dynamics are exact and short-horizon search is globally optimal, whenever the route to the goal initially moves away. They introduce Anchored Planning, a training-free method that reuses the frozen model's own offline trajectories to select an intermediate target. Across Cube, PushT, Reacher, and TwoRoom, it outperforms the LeWM planner and additional final-goal search on long-range goals.

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