Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-7· 0 citations· 43 references
Abstract
Robotic manipulation in complex scenes requires robust reasoning and fine-grained part-level control. While recent language-guided policies have demonstrated strong manipulation capabilities, most existing benchmarks lack explicit obstacles and other sources of interference, and do not provide corresponding obstacle-aware solutions. To address this, we introduce MOBInstruct, a benchmark for part-level manipulation in cluttered environments with obstacles, along with a hierarchical framework, MOBA, for obstacle-aware execution. The MOBA framework consists of high-level modules for task planning and scene reasoning, as well as low-level modules for part-level action execution with position-aware noise initialisation. Within our framework, we evaluate representative baselines on the MOBIn-struct benchmark. Experiments demonstrate effective obstacle removal and robust task completion in multi-object scenes under diverse environmental conditions, including lighting and tabletop textures. Project website: https://j-x-chen.github.io/mobinstruct/
CoMuRoS enables runtime, event-driven replanning on physical robots and supports flexible multi-robot and human-robot collaboration across diverse scenarios.
Suraj S. Borate, Bhavish Rai B, Vipul Pardeshi et al.· Frontiers in Robotics and AI· 0 citations
IMBENCH is introduced, a benchmark designed to evaluate intuitive manipulation as an integrated capability spanning perception, physical reasoning, action generation, and iterative execution, and position IMBENCH as a step toward evaluating and enabling more integrated, adaptive physical intelligence.
Evidence Acquisition and Feasibility Gating (EAFG) is proposed, a framework that acquires visual evidence through VLM-generated exploratory subgoals and TAMP-based execution and applies a feasibility gate to decide whether to proceed with task planning, acquire further evidence, or halt.
Tsunehiko Tanaka, Matthew Stephenson, Alistair Macvicar et al.· 0 citations
In Object Goal Navigation task, existing methods are typically restricted to static and single-floor environments, ignoring cross-floor topologies and dynamic pedestrian, which limits their real-world deployment. To address these limitations, we propose ZONDA, a zero-shot object navigation with dynamic avoidance framework. In particular, ZONDA integrates three core components: (i) Heuristic multi-floor planning: from height-difference traversable maps, enables stair traversal and cross-floor exploration without a platform-specific learned controller; (ii) Multi-view target verification: cross-checks multi-scale observations with a vision-language model, significantly reducing false positives; and (iii) Dynamic pedestrian avoidance: explicitly tracks and predicts moving pedestrians to generate anticipatory behaviors. Evaluated on a real Direct Drive Tech TITA biped robot and extensive simulations on HM3D and MP3D, ZONDA achieves significantly improved results. Moreover, ZONDA can maintain robust navigation on the dynamic benchmark HM3D-DYNA compared to the existing baseline.
RoomWright is presented, an agentic usage-driven framework for generating 3D scenes represented entirely as code for embodied interaction, providing interactive environments for embodied AI and policy learning.
Zijian Xiao, Zipeng Ye, Jin-Kun Hao et al.· 0 citations
Despite recent advances in general-purpose robotic manipulation, real-world multi-object clutter remains challenging to handle for today's prevalent approaches. The problem scales in complexity due to more objects and collisions, more unpredictable contact physics, distractors, and task ambiguity. Bridging this gap to real-world deployment requires effective scene abstractions; yet today, producing such abstractions requires extensive task-specific manual engineering, which does not scale. These abstractions are costly to generate and difficult to adjust or fine-tune. We instead propose a plug-and-play fix to automatically generate scene-specific, task-specific, adaptively updating abstractions on top of existing planning and control stacks. LLM-guided Environment Simplification (LENS) produces a de-cluttered abstracted scene representation by merging (e.g., stacked objects) or pruning (e.g., distant objects) scene entities in a closed loop in response to task progress. These dynamic, task-relevant abstractions are versatile and easy to use. In our experiments, we show that LENS improves classical planning, model-based control, and a vision-language-action model, across a diverse set of highly cluttered manipulation scenes. Project website: https://lens-2026.github.io/.
