The paper introduces RECALL, an active, continual learning paradigm for Vision-Language-Action models that addresses the inefficiencies of passive imitation learning. Unlike traditional methods that require robot failures to trigger data collection, this approach uses uncertainty-guided recovery demonstrations to proactively identify states needing supervision. The authors demonstrate that this targeted data collection leads to more efficient fine-tuning compared to passively collected demonstrations. However, the study reveals that fine-tuning exclusively on this active recovery data causes catastrophic forgetting of previously learned behaviors. To mitigate this issue, the work evaluates continual learning techniques such as replay-based data mixing and elastic weight consolidation. These experiments highlight the critical tradeoffs between plasticity for new tasks and retention of existing capabilities in autoregressive VLAs. Ultimately, the research establishes that while uncertainty-guided recovery improves adaptation efficiency, incorporating targeted new data into large robot policies presents significant open challenges.
RECALL: Active Lifelong Learning for Vision-Language-Action Models
Dyna Robotics releases Dyna-2, a world-action model pre-trained on 1 million hours of human video
Dyna Robotics has released Dyna-2, a world-action model for robot manipulation that was pre-trained on more than one million hours of egocentric human video. The company demonstrates that ordinary human video can substitute for action-labelled data by establishing a scaling law from 1,000 to 1,000,000 hours.
Overview of simulation engines for Physical AI: MuJoCo, Isaac Sim, and Newton
This article provides an overview of the current state of simulation for Physical AI, explaining how simulation bridges the data gap for robotics by enabling scalable, photorealistic training environments. It outlines a three-computer paradigm (training, simulation, on-robot) and categorizes key simulation engines based on their specific capabilities and use cases.
RoboTTT scales robot policies to 8K-timestep context
Researchers introduce RoboTTT, a training recipe that extends visuomotor context for robot foundation models to 8,000 timesteps without increasing inference latency. The method integrates Test-Time Training into Vision-Language-Action policies by using fast weights updated via gradient descent during both training and inference.
RoboTTT scales robot policies to 8K-timestep context
NVIDIA introduces RoboTTT, a robot foundation model and training recipe that extends visuomotor context to 8,000 timesteps without increasing inference latency. By integrating Test-Time Training into Vision-Language-Action policies, the system compresses history into weight space using fast weights updated by gradient descent.
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
This paper proposes an active, continual learning paradigm for Vision-Language-Action (VLA) models to address the inefficiencies of passive imitation learning. The authors demonstrate that uncertainty-guided data collection improves fine-tuning efficiency but causes catastrophic forgetting when recovery data is used exclusively.