arXiv:2511. 17649v4 Announce Type: replace-cross Abstract: Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday human-built environments.
arXiv:2609.13152v1 Announce Type: new Abstract: Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experim...
The study investigates how different observation fidelities affect the problem‑solving performance of embodied large language model (LLM) agents. Using the Lockbox mechanical puzzle, agents were tested with raw RGB, RGB‑D, and perfect ground‑truth symbolic observations in both physical and simulated environments. Surprisingly, agents performed best with raw RGB input and worst with perfect observations; introducing moderate perceptual noise in simulation further improved success rates, suggesting that some errors can mitigate repetitive action loops.
World-Action Models (WAMs) jointly predict future world states and robot actions for diverse manipulation tasks, but they can suffer from Stage Confusion—generating plausible yet misdirected rollouts that drift to the wrong task stage. TaskWAM introduces a closed-loop, stage-aware framework using Mode Tokens and State Tokens to represent fine-grained task requirements and execution progress, steering generation toward the intended future. The authors also present StageBench, a multi-stage manipulation benchmark, and show through extensive simulation and real-world experiments that TaskWAM reduces confusion, matches state-of-the-art performance, and transfers effectively across various robot embodiments.
arXiv:2610.14106v1 Announce Type: cross Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically ground...
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To investigate this, we...
arXiv:2610.10409v1 Announce Type: cross Abstract: General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities car...
arXiv:2606. 02951v1 Announce Type: cross Abstract: Deploying language-driven agents in robotics requires evaluations that reflect real-world task demands: natural-language instructions with reproducible outcomes.
arXiv:2608.22971v2 Announce Type: replace Abstract: Embodied AI has emerged as a key paradigm for autonomous systems operating in complex physical environments. While end-to-end models have establish...
Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and physical realism. Large language model (LLM)-based approaches can interpret diverse open-vocabulary instructions and compose high-level action plans, but they often generate motions that violate physical constraints.
ReactHuman is a physics‑grounded benchmark that tests whether multimodal large language models (MLLMs) can make immediate, safety‑critical decisions in simulated humanoid scenarios involving sudden household hazards. The benchmark includes 17 event families, over 1,000 reproducible scenes generated from 240 Hz rigid‑body simulation, and a five‑metric suite evaluating reactions on reasonableness, safety, and physical grounding. Evaluation of seven MLLMs reveals that reactive safety remains unsolved, with models frequently mishandling hazards, relying on appearance over motion, and missing key interception points.
arXiv:2605.13119v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burde...