News & Research

arXiv:2606.16022v1 Announce Type: new Abstract: We introduce $\lambda$-Reachability, a scalable approach to Hamilton--Jacobi safety analysis for high-dimensional robotic systems. Unlike prior discounted formulations that rely on fixed one-step Bellman updates, $\lambda$-Reachability employs a stochastic multi-step estimator of the safety value, using a geometrically distributed rollout horizon together with a randomly absorbed terminal. Conceptually analogous to TD($\lambda$), $\lambda$-Reachability interpolates between local self-consistency updates and long-horizon max-over-trajectory safety targets via an interpretable horizon-control parameter. Unlike TD($\lambda$), where the terminal value is always incorporated in learning targets, the terminal safety value in $\lambda$-Reachability is only used at a probability controlled by parameter $\delta$. We formally show that for $\delta<1$, the update induces a contraction mapping that allows temporal-difference learning; as $\lambda \to

arXiv:2606.15997v1 Announce Type: new Abstract: Lower-limb exoskeletons require actuation systems that can provide accurate joint torque control while preserving low mass and encumbrance. Conventional architectures often rely on independently actuated joints and joint-level torque sensors, increasing system complexity and weight. This paper presents a novel differential actuation architecture for hip-knee flexion/extension, enabling cooperative torque sharing between two motors via a linear differential mapping between motor and joint. To compensate for transmission losses, a model-based friction estimation strategy is developed and experimentally implemented, allowing accurate joint torque estimation without the need for torque sensors. The proposed solution is validated on a physical prototype, demonstrating the feasibility of sensorless torque estimation in a differentially actuated hip-knee module of a lower-limb exoskeleton.

arXiv:2606.15918v1 Announce Type: new Abstract: Humanoid robots performing in-field manipulation tasks, such as robotic apple harvesting, face severe energy constraints that directly limit the number of reaching motions that can be executed per battery charge. This paper presents an end-to-end, energy-aware reinforcement learning framework for the 7-degree-of-freedom left arm of the Unitree~G1 humanoid robot, combining a physics-based, experimentally identified electrical power model with a Soft Actor-Critic (SAC) policy trained in a Pinocchio-based rigid-body dynamics simulator. The RL policy operates on an incremental joint-position action space and is trained with a Hybrid Constellation Reward that combines a four-point end-effector constellation distance with a torque-norm energy proxy; after % $5\times10^6$ training it reaches a $69.9\%$ success rate over $1\,000$ random targets in kinematic simulation, at a mean energy of \SI{98.16}{\joule} on successful episodes. Finally, on the

arXiv:2606.15915v1 Announce Type: new Abstract: Accurate prediction of electrical power consumption is essential for energy-aware motion planning, battery management, and thermal monitoring in battery-powered humanoid robots. This letter presents a physics-based, linear-in-parameters model for the electrical power consumption of the seven-degree-of-freedom left arm of the Unitree~G1 humanoid robot. The proposed formulation combines actuator loss terms with a baseline-torque correction that captures changes in gravity-compensation load and enables accurate prediction of negative net power trajectories. Pairwise interaction terms are introduced to model power coupling during simultaneous multi-joint motion. Model parameters are identified from experimental data collected on a physical Unitree~G1 using onboard power measurements as the regression target. Across 897 trajectories covering single-joint and coordinated arm motions at multiple speed levels, the identified model achieves $R^2 =

arXiv:2606.15909v1 Announce Type: new Abstract: Modern tactile-language models (TLMs) have shown potential for robot learning tasks, such as material and texture recognition. However, for contact-rich scenarios, these TLMs struggle to understand the physical properties of dynamic objects, such as rotation and sliding directions. For instance, our preliminary experiments reveal that popular TLMs, such as Sparsh and AnyTouch2, exhibit weak performance on basic rotation direction reasoning from GelSight Mini tactile data. This surprising gap inspires us to explore a novel research question: Can we inject physically grounded geometric priors into TLMs to enable reliable contact orientation reasoning of dynamic object properties? To this end, we propose GeoTLM, a novel geometric representation-guided TLM for the perception of dynamic contact events. Our key idea is to preserve and structure tactile shear-field geometry before language-level reasoning, rather than forcing low-resolution tact

arXiv:2606.15896v1 Announce Type: new Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective. We instead treat these functions through distinct mechanisms: rewards for task specification, constraints for operational limits, energy minimization for gait preference, and exteroceptive perception for adapting energy use to terrain difficulty. We show that these components jointly enable efficient, terrain-adaptive locomotion, and that removing each component exposes a distinct failure mode. Our formulation removes explicit gait priors (including air-time, contact-count, and foot-clearance targets) in favor of emergent behavior. Compared to a conventional complex-reward baseline, our formulation achieves comparable terrain traversal while reducing cost of transport by 56% and operational-limit violations by 96%. The res

arXiv:2606.15846v1 Announce Type: new Abstract: Deep reinforcement learning has shown strong potential for robot navigation, but its practical deployment is still limited by the long wall-clock cost of policy training. This paper presents FlashNav, a GPU-first framework for ultra-fast range-based robot navigation training. To the best of our knowledge, FlashNav is the first DRL-based robot navigation framework that reaches seconds-level policy training, with the fastest deployable policy trained in less than 20 seconds. The key idea is to align simulation with the navigation MDP: FlashNav preserves the essential components for velocity-level navigation, including occupancy geometry, range sensing, goal-conditioned control, robot motion dynamics, collision handling, termination, and reset, while removing unnecessary rendering and high-fidelity physical details from the training loop. Built on a batched bitmap simulator and a fully GPU-resident training pipeline with our FastDSAC learner

arXiv:2606.15768v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene. World-Action Models (WAMs) address this limitation by conditioning policies on predicted futures, yet existing approaches typically rely on computationally expensive video generation with substantial pixel-level redundancy. We present LaWAM, a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video. At the core of LaWAM is a latent-action-conditioned Latent World Model (LaWM). We obtain LaWM by training a latent action model in the latent space of a pretrained vision foundation model and repurposing its forward decoder to predict future observation features for scene evolution. LaWAM then conditions action generation on these predicted latent visual subgoal

arXiv:2606.15691v1 Announce Type: new Abstract: Causality in robotics aims to produce more interpretable and flexible robot behaviours by enabling robots to predict the consequences of their actions; however, deploying causal models with existing systems (e.g., navigation) operating in real environments remains understudied. This paper addresses the challenging problem of transferring causal models in real-robot experiments for a navigation scenario. We study this problem in two ways: (i) using the causal model as an offline evaluation module that predicts the competence of recorded real-robot navigation trajectories and relates it to quantitative navigation performance, and (ii) using the causal model as an online adaptation module that intervenes when the predicted competence of the default navigation is low. We validate our approach in a physical service robot that patrols around corridors. We show that the predicted competence correlates positively with path efficiency, and negativ

arXiv:2606.15685v1 Announce Type: new Abstract: Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. Compared with conventional continual learning, ECL suffers from more severe catastrophic forgetting. Feature drift accumulated under closed-loop control progressively propagates through sequential decision-making, leading to degradation of previously learned behaviors. A key challenge in ECL lies in structured skill reuse across continually evolving tasks, since existing methods primarily focus on skill learning without explicitly organizing them for coherent task execution. To address this issue, we propose SCE, a Skill-Compositional Experts framework for ECL. SCE builds a skill base via Compositional Skill Grounding (CSG), which decomposes task demonstrations into reusable skills. Based on this, Dual Execution-and-Transition Experts (DETE) enable new task learning t

arXiv:2606.15654v1 Announce Type: new Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive. We introduce PO-PDDL, a symbolic formulation of POMDPs that preserves the relational structure and LLM-friendly syntax of the Planning Domain Definition Language (PDDL), while explicitly modeling partial observability, stochasticity, and beliefs. Building on this formulation, we propose a demonstration-driven pipeline for learning PO-PDDL models. The proposed method reconstructs latent symbolic state trajectories from real-robot execution videos, identifies partial observability via inconsistencies between inferred states and visual observations, and learns stochastic transition and observation models accordingly. The resulting PO-PDDL domains are reusable across tasks and enable online beli

arXiv:2606.15645v1 Announce Type: new Abstract: Soft robots leverage compliant materials to generate motion through controlled elastic deformation, making them ideal for delicate tasks such as underwater exploration and biomimetic marine systems. Although hydraulic/pneumatic actuation remains pivotal for such systems, the lack of systematic design frameworks has hindered the development of robots capable of complex 3D motion, such as fish-like swimming. This work introduces a topology optimization method to automate the design of a hydraulic soft fish tail, explicitly addressing the design-dependent coupling between fluidic actuation and structural deformation. We use a Darcy law-based model augmented with a drainage term to simulate spatially varying hydraulic pressure loads, translating these into consistent nodal forces via finite element analysis. The employed robust multi-criteria optimization formulation balances deformation efficiency, fluid-structure interaction, geometric manu

arXiv:2606.15594v1 Announce Type: new Abstract: We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models. Our approach trains an action-conditioned joint-embedding world model with compact Markovian latent states, enabling efficient gradient-based trajectory optimization through learned latent dynamics. To enforce safety for the true system despite imperfect latent predictions, we inform a GPU-accelerated system level synthesis (SLS) robust MPC scheme with conformal prediction to obtain calibrated latent error bounds and robust latent-space constraint sets. We further learn and conformalize a latent constraint checker, allowing the SLS planner to impose probabilistic safety constraints during closed-loop execution. We evaluate our method on vision-based control tasks, where it improves both goal-reaching performance and safety over latent world-model and safe-planning baselines.

arXiv:2606.15587v2 Announce Type: replace Abstract: Expert demonstrations are widely assumed to be the gold standard for robot imitation learning. Yet for fine-grained manipulation such as insertion, stacking, and alignment, we uncover a counterintuitive failure mode: fluent demonstrations can be poor teachers. A skilled teleoperator compresses the decisive moments of alignment and recovery into a brief temporal window, leaving the policy flooded with redundant free-space motion and starved of supervision exactly where precision determines success. We address this bottleneck at two levels. At the data level, slowing down near alignment and resampling critical segments both help, yet the gain comes mainly from broadening the coverage of recovery states the policy must learn, not from reweighting frames it already has. Such data-side fixes, however, leave the policy's per-frame view untouched: a single image still maps directly to an action, and the local motion that governs correction s

arXiv:2606.15568v1 Announce Type: new Abstract: Recent advancements in Vision-Language-Action (VLA) models have demonstrated impressive generalist capabilities in robot manipulation, yet these policies can be brittle under out-of-distribution spatial and semantic perturbations. While human teleoperation offers reliable recovery, it can demand high cognitive load and precise manual control, and existing policy steering methods often require auxiliary models or sampler modifications. In this work, we introduce Shared Autonomy for Policy Steering (SAPS), a framework that blends real-time human teleoperation commands with pretrained policy actions at the action level. SAPS requires no policy retraining, auxiliary dynamics models, or architectural modifications. We propose and evaluate three arbitration strategies to balance human and VLA policy control, including a dynamic Cosine-similarity arbitration strategy that computes the geometric agreement between human and policy actions. Across