Robot State
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Related Articles from SNS
Continuum Robot State Estimation with Actuation Uncertainty
arXiv:2601.04493v3 Announce Type: replace Abstract: Continuum robots are flexible, slender manipulators well suited for confined surgical environments. In these settings, unknown interaction forces and model uncertainty significantly affect robot shape, motivating state estimation from external observations. Existing estimation methods either neglect actuation modeling or rely on simplified deterministic actuation models.
Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling
arXiv:2606.09476v1 Announce Type: new Abstract: Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends only on a subset of the state. We propose Goal-Set Hindsight Relabeling (GS-HER), a predicate-level generalization of HER in which achieved states certify query-defined goal sets rather than singleton goal states. A binary query specifies which variables define success, making the goal predicate an...
StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
arXiv:2605.03927v2 Announce Type: replace Abstract: Vision-language models (VLMs) have shown remarkable performance in various robotic tasks, as they can perceive visual information and understand natural language instructions. However, when applied to robotics, VLMs remain subject to a fundamental limitation inherent in large language models (LLMs): they struggle with numerical reasoning, particularly in object detection and object-state localization. To explore numerical reasoning as a...
Inverse Manipulation through Symbolic Planning and Residual Operator Learning
arXiv:2606.05248v1 Announce Type: new Abstract: Inverting a robotic task requires more than reversing symbolic state transitions or rewinding motor trajectories. In robot manipulation tasks, symbolic inverse plans often fail to fully restore the effects of forward executions under continuous interaction dynamics.
Trajectory Planning for Non-Communicating Mobile Robots using Inverse Optimal Control
Announce Type: new Abstract: To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states.
Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs
arXiv:2605.30617v1 Announce Type: new Abstract: Robust and efficient state estimation is crucial for perception, navigation, and control in robotics. State estimation problems are conveniently modeled using the factor-graph framework as enabled by modern software packages such as GTSAM or g2o. However, the standard solvers included in such frameworks are local and may converge to poor local minima, posing significant safety concerns.
Contrastive Representation Regularization for Vision-Language-Action Models
Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs). However, their representations arguably remain suboptimal, lacking sensitivity to robotic signals such as control actions and proprioceptive information. To address the issue, we introduce Robot State-aware Contrastive Loss (RS-CL), a simple and effective representation regularization for...
World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis
arXiv:2606.05979v1 Announce Type: new Abstract: We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the \emph{world modeling interface} to learn from extensive egocentric videos as in the world-action model (WAM) and the \emph{language reasoning} capacities to solve complex long-horizon tasks as in...
Nvidia, Unitree and Sharpa unite to design humanoid robot that can perform ‘real work’
Nvidia, Unitree and Sharpa unite to design humanoid robot that can perform ‘real work’ The tech trio team up to create a state-of-the-art robot reference design allowing researchers to build, fine-tune and deploy skills faster The new design, called H2+ or Isaac GR00T, will support industry-wide humanoid robotics research by streamlining the full development workflow for developers, including data collection, policy training and real-world deployment. “For agentic systems, robotic systems...
Learning from Demonstrations over Riemannian Manifolds using Neural ODEs: An Extended Abstract
Announce Type: new Abstract: Learning from demonstratins (LfD) is usually performed over Euclidean spaces, while the robot state, e.g. orientation, naturally evolves over curved spaces. Therefore, to ensure natural, complex motion generation, we investigate learning from demonstrations over Riemannian manifolds that are capable of encoding both position and orientation data.