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STEER: Flexible Robotic Manipulation via Dense Language Grounding

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arxiv 2411.03409 v1 pith:7FULFYDP submitted 2024-11-05 cs.RO cs.AI

Laura Smith , Alex Irpan , Montserrat Gonzalez Arenas , Sean Kirmani , Dmitry Kalashnikov , Dhruv Shah , Ted Xiao This is my paper
classification cs.ROcs.AI
keywords steertrainingadaptbehaviordenseflexibleintelligentlylanguage
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The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that bridges high-level, commonsense reasoning with precise, flexible low-level control. Our approach translates complex situational awareness into actionable low-level behavior through training language-grounded policies with dense annotation. By structuring policy training around fundamental, modular manipulation skills expressed in natural language, STEER exposes an expressive interface for humans or Vision-Language Models (VLMs) to intelligently orchestrate the robot's behavior by reasoning about the task and context. Our experiments demonstrate the skills learned via STEER can be combined to synthesize novel behaviors to adapt to new situations or perform completely new tasks without additional data collection or training.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation

    cs.RO 2026-07 conditional novelty 6.0 of 10

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  2. See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs

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    S2 improves generalization in vision-language-action models by using goal-preserving refined language guidance and explicit visual evidence budgets, raising mean subtask success from 54.2% to 79.0% on eight real-robot...

  3. FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies

    cs.RO 2026-05 unverdicted novelty 6.0 of 10

    FineVLA unifies robot datasets into 47k fine-grained trajectories, adds a VLM annotator and benchmark, and shows that mixing fine-grained and goal-level instructions improves steerable control without hurting task success.

  4. Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control

    cs.RO 2026-02 unverdicted novelty 6.0 of 10

    Steerable VLAs trained on rich synthetic commands at subtask, motion, and pixel levels enable VLMs to steer robot behavior more effectively, outperforming prior hierarchical baselines on real-world manipulation and ge...

  5. CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models

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    Counterfactual language-action relabeling raises instruction-following success from about 26% to 53% in real-world navigation tests.

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