HIDRA is a hierarchical dual-routing attention framework designed for replay‑free lifelong imitation learning in robotics. It separates instruction‑level expert retrieval from context‑dependent refinement and adds key‑level geometric regularization to keep tasks aligned yet distinct, thereby reducing routing interference. Experiments on LIBERO benchmarks show that HIDRA outperforms both replay‑free and replay‑based baselines, improving AUC, forward transfer, and maintaining low forgetting, especially under high interference and language variation.
arXiv:2606. 30266v1 Announce Type: cross Abstract: Motion-language agents must possess the bidirectional capability to both understand human movement (motion-to-text, M2T) and generate it from natural language (text-to-motion, T2M).
arXiv:2606. 03598v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation.
arXiv:2607. 02466v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale.
The paper introduces SAME (Stabilized Mixture-of-Experts) to address challenges in Multimodal Continual Instruction Tuning (MCIT) for large language models. SAME mitigates router drift by decomposing routing dynamics into orthogonal subspaces and updating only task-relevant directions, while preventing expert drift through curvature‑aware scaling that uses historical input covariance without rehearsal. The method also employs adaptive expert activation to freeze selected experts during training, reducing redundant computation and cross‑task interference, and demonstrates state‑of‑the‑art performance on a new long‑task‑sequence benchmark.
arXiv:2608. 19589v1 Announce Type: cross Abstract: Pretrained Vision-Language-Action models provide a strong foundation for robot learning, but sequentially adapting them to diverse skills can perturb the representations and velocity mappings used by previous skills, leading to catastrophic forgetting.
RIFAR is a new continual learning method for robots that uses reliability screening and drift-aware replay to mitigate forgetting while keeping storage low. It reconstructs past trajectories from short demonstration prefixes and employs a frozen inverse-dynamics model to verify action‑visual consistency. In experiments on LIBERO suites and real‑world tasks, RIFAR outperforms previous generative replay approaches, achieving high performance with only a small fraction of stored steps.
RARA (Retrieval‑Anchored Representation Alignment) is a two‑stage method for fine‑tuning pre‑trained robot policies on new tasks with few demonstrations. It first retrieves and aligns representations from pre‑training that match the target task’s intent and motion, then fine‑tunes while constraining changes to those retrieved representations. Experiments on Diffusion Policy and GR00T across 13 tasks in simulation and on a real robot show RARA improves in‑distribution success by 9.5 points and out‑of‑distribution success by 13.6 points, outperforming all baselines.
The paper introduces Hierarchical Skill Retrieval (HSR), a framework that decomposes a target manipulation task into candidate skill sequences and evaluates each plan for semantic plausibility and skill reliability. HSR combines subtask-level language retrieval with behavior-feature reranking to select demonstrations that are both relevant and compatible with the target task, followed by a two-stage pretraining and finetuning pipeline for policy adaptation. Experiments on the LIBERO benchmark and real-world robot tasks show that HSR improves average success rates by 10.3% and 21.3% over the strongest baseline, demonstrating the effectiveness of structured skill-level retrieval for data-efficient Vision‑Language‑Action adaptation.
The paper introduces Adaptive Agents via Latent Topologies (AALT), a method for active imitation learning that selects demonstrations based on their expected impact on start‑to‑goal connectivity rather than generic information gain. AALT builds a latent topology of hub states and learned behaviors, identifies high‑value bridge demonstrations that can solve many tasks simultaneously, and uses these to condition a diffusion policy for planning. In a simulated UR5e robot retrieval task with 72 start‑goal pairs, AALT achieved 100% success after only three demonstrations, outperforming baselines that required many more queries.
arXiv:2610.18596v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more...
ACCRUE is a continual off‑policy reinforcement learning framework designed for robot foundation models. It keeps lightweight actor and critic networks across tasks, reusing behavioral and value knowledge from previous RL stages while preserving prior behavior through actor distillation on a small set of retained episodes. Experiments on contact‑rich assembly tasks in simulation and on real robots show that ACCRUE speeds up learning of new tasks, nearly eliminates forgetting, and cuts real‑robot training time by 31.0% to 68.5%.
arXiv:2606. 26183v1 Announce Type: cross Abstract: Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge.