Vision-language-action models enable instruction-conditioned robot manipulation, but they typically cannot improve from experience after deployment. This paper proposes VLRLA, a framework that equips frozen VLA models with in-context reinforcement learning by conditioning action generation on a recurrent memory of past observations, actions, rewards, and termination signals. Using algorithm distillation on constructed learning histories, VLRLA aims to gradually improve task success during deployment without updating network parameters.

Wrist-worn IMUs power everything from gesture recognition to clinical motor assessment, yet most prior benchmarks evaluate each task in isolation, leaving unclear whether a single pretrained representation can serve all of them. This paper introduces IMU-Bench, a unified evaluation of ten tasks spanning kinematic estimation, semantic retrieval, activity recognition and Parkinson's assessment, and proposes InrtlWrist, a masked autoencoder pretrained on recorded and simulated wrist motion. The core finding is that pretraining yields broadly useful features—leading on nine of ten frozen-probe tasks—but that supervision from one downstream task rarely helps unrelated ones, distinguishing universal representation quality from task compatibility.

Multimodal agents need persistent memory to act coherently over long horizons, yet forming and managing memory across heterogeneous, high-dimensional observations—video, audio, 3D geometry, and GUIs—has lacked a unifying analytical lens. This survey introduces a two-axis lifecycle framework that classifies how experience is represented (source, textual, structured, latent, or parametric) and how memory states and policies evolve ($T1$ Static, $T2$ Dynamic, or $T3$ Self-Evolving). By analyzing 297 architectures, the authors identify key trade-offs between fidelity and efficiency and expose the striking scarcity of systems that evolve their own memory policies at deployment time.

Robota-Embed tackles the problem of retrieving relevant physical experience for robots by learning a single embedding space that jointly encodes vision, language, and action from both the current state and a future trajectory segment. The core insight is that a useful retrieval key must capture not just task intent and visual scene, but also the robot's ongoing motion and execution stage, because the goal is to retrieve an appropriate continuation rather than simply a similar observation. A new multi-source benchmark and two downstream applications—in-context policy conditioning and memory evolution around a frozen large policy—show that this unified, action-aware representation significantly outperforms vision-language-only baselines and improves both success rates and token efficiency.

Wedge addresses the inefficiency of generic environment generation for embodied navigation: pretrained agents fail for different environmental reasons, so simply adding more diverse or realistic training scenes may not address their actual shortcomings. The paper proposes a policy-conditioned generation framework that diagnoses an agent's specific weaknesses by contrasting its successful and failed rollouts, uses an LLM to propose executable environmental features (such as target visibility or blind-path ratio), validates them via success-rate gaps, and generates targeted training environments. This approach is tested across three heterogeneous agents—an RL policy, an imitation-learned model, and a black-box VLM—showing consistent gains in ProcTHOR and transfer to HM3D.

VLX-Go targets embodied visual tracking in distractor-rich shared spaces by casting local motion as vision-language-conditioned short-horizon waypoint prediction. It combines frozen DINOv3 and SigLIP features, multiscale temporal context, and a transformer backbone to predict $K=8$ robot-centric waypoints. A staged training recipe—vision-language alignment, multimodal reasoning mid-training, and tracking specialization—drives a large improvement over the OmTrackVLA baseline, while preliminary DAgger fine-tuning provides modest further gains. This matters because existing methods suffer severe success-rate drops when moving from single-target to distractor tracking.

NavAble tackles a critical data scarcity problem in assistive navigation: standard vision datasets largely ignore accessibility-critical objects such as pedestrian signals and door-activation buttons, leaving blind and low-vision assistive systems with unreliable perception. The authors introduce a large-scale dataset and a synthetic generation pipeline that combine 36K curated real-world images, 5.5K newly collected egocentric frames, and 452K synthetic frames across 11 BLV-relevant classes. By filling this gap, NavAble provides a foundation for training segmentation models that can recognize navigationally important objects from diverse assistive-device viewpoints.

Multi-agent motion planning with diffusion models currently demands costly training on large feasible-trajectory datasets, making learned planners map-specific and data-hungry. This paper introduces TFDP, a training-free diffusion-style planner that replaces global neural score networks with analytical local scores derived from smoothness, obstacle-avoidance, inter-agent separation, and velocity-limit terms. By exploiting the spatial and temporal locality of trajectory refinement, the method scales to hundreds of agents and obstacles in seconds without any demonstrations or neural network training.

Quadrupedal locomotion requires balancing command tracking, stability, and energy efficiency, yet conventional RL bakes these trade-offs into a static scalar reward that cannot change after training. PROMO proposes a preference-conditioned multi-objective framework that takes the relative priority vector $w_t \in \Delta^{K-1}$ with $K=3$ as a runtime input to a single policy, while keeping embodiment-specific locomotion priors fixed. On a Unitree Go2, the same policy transferred zero-shot modulates behavior through preference changes alone, cutting specific energy by up to $30.4\%$, position error by $38.7\%$, and peak body-attitude deviation by $59.0\%$.

Policy-gradient methods suffer from high variance, while pathwise actor updates require accurate action derivatives that standard value-function training does not guarantee. FERPO addresses this by deriving an entropy- and KL-regularized target action distribution from critic values alone, then fitting the policy with forward KL via self-normalized importance sampling---never differentiating the critic with respect to actions. The method targets massively parallel on-policy robot learning, where reliable policy improvement is critical for both sample and wall-clock efficiency.

Autonomous driving system designers face a persistent dilemma: should they build a decomposed modular pipeline or train an end-to-end neural policy? This survey-style paper addresses that question by comparing modular, imitation-learning, reinforcement-learning, and hybrid architectures across interpretability, scalability, and robustness. Its central contribution is a practical twelve-criterion, four-dimensional framework intended to guide practitioners in selecting the right paradigm for a given deployment context.

World Action Models (WAMs) jointly predict visual futures and robot actions, yet they often hallucinate locally plausible trajectories that never execute the critical state transition needed to finish the task. This paper identifies "task-incomplete imagination" as a failure mode arising from the mismatch between long-horizon video pretraining and short-horizon control generation, and proposes Completion Aware Guidance (CAG), a training-free sampling method that up-weights instruction tokens already attended to by the model. Across two WAM architectures, CAG boosts success rates and cuts incomplete-imagination failures, offering a lightweight inference-time fix for a structural objective mismatch.

Latent world models plan by scoring action sequences with latent-space distances, yet physical success criteria demand precision finer than these models typically encode. This work proposes a training-only auxiliary loss that supervises encoder and predictor outputs against the exact success-criterion quantities via a shared linear head, then discards the head at test time so planning remains unchanged. On manipulation tasks the approach yields statistically significant absolute gains of 3-5%, with the key insight that a task's success criterion itself should dictate what the latent state must retain.

Unmanned aerial vehicle swarms operating with independent edge-deployed language models face a dual challenge: reasoning context degrades as interaction histories grow, and indiscriminate information sharing wastes limited bandwidth. This paper proposes a distributed architecture in which each UAV maintains structured atomic notes in bounded core, local, and peer-specific memory queues, coupled with a deterministic Interest-Aware Gossip Engine that forwards messages based on semantic novelty and recency. The work matters because it targets the practical gap between centralized LLM coordination, which introduces bottlenecks and single points of failure, and fully decentralized control, which risks context degradation and communication collapse.

Standard grasp synthesis systems freeze their parameters after offline training and struggle with unfamiliar objects. This paper presents a continual-learning framework that replaces the fixed parametric scorer with a memory-based non-parametric model in a learned embedding space, augmented by recalling and transferring user demonstrations to new scenes via geometric registration. Instead of gradient-based finetuning, the robot adapts by adding grasp outcomes and demonstrations to structured memories, which enables fast, lightweight updates during real-world deployment with limited forgetting.

Coordinated multi-humanoid loco-manipulation is notoriously difficult because it couples high-dimensional whole-body control with decentralized decision making and scalability. MASkillBlender addresses this by learning a shared decentralized high-level multi-agent RL policy that blends frozen, pre-trained single-humanoid primitive skills, eliminating the need for task-specific motion references or painstaking per-task reward engineering. The authors further introduce a permutation-based data augmentation strategy and prove, under a homogeneous Markov game formulation, that it preserves the policy-gradient direction. Simulated experiments across cooperative manipulation and locomotion tasks suggest broad versatility across robot embodiments and simulators, though the work stops short of real-world validation and is limited to small, homogeneous teams.

In model-predictive control with the cross-entropy method, a learned world model scores candidate action sequences and refits the Gaussian proposal for the next search iteration, creating planner-induced query shift. The authors disentangle these two roles by saving every CEM candidate pool from four surrogate models, executing all candidates in a resettable environment for a ground-truth ranking, and rescoring each pool with every model in a crossed design; they then splice the first elite set with environment-ranked elites to trace causal consequences through later iterations. Their results show that standard proposal distances contract even as ranking agreement stays near chance and mean disagreement grows, and that a single environment-guided elite update lowers final selected cost—demonstrating that world-model quality must be judged by its effect on the proposal, not just on candidate scoring.

Spacecraft rendezvous and proximity operations currently require specialized engineers to translate high-level mission intent into dynamically feasible trajectories, creating a scalability bottleneck for distributed space systems. This paper introduces OrbitTAMP, a hierarchical task-and-motion planning framework that leverages pretrained LLMs to map natural-language commands into a partial mission specification, then systematically completes unspecified behavior, timing, and waypoint decisions via a domain-specific behavior graph and planning modules. Final trajectory optimization ensures dynamical feasibility and safety, aiming to provide an auditable, scalable language interface for spacecraft operations.

Vision-language-action policies often fail on partially observable, long-horizon manipulation because the current image alone does not determine the correct action. This paper reformulates robotic memory as an optimisation problem: the optimal memory should maximise the conditional mutual information $I(a_t; m_t \mid o_t)$ between action and memory given the current observation. To realise this, the authors propose Divide-and-Remember (D&R), a recursive lightweight selector that retains the top-$K$ most action-relevant tokens from an unbounded history while staying in the pretrained VLA token space. On the RoboMME benchmark and in real-robot experiments, D&R achieves strong gains with budgets as small as 64 tokens, offering both theoretical clarity and empirical progress on memory-augmented visuomotor control.

Screw Attention replaces abstract graph edges in policy transformers with rigid-body spatial algebra. Each token carries a body pose, and messages between tokens are transported by relative poses and joint screws, making the layer equivariant to independent frame changes at every link. This yields compact learned policies that outperform larger unstructured networks and remain robust to the kinematic description conventions that break existing architectures.