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
ZONDA, a zero-shot object navigation with dynamic avoidance framework, integrates three core components and 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, Zi-Peng Ye, Jin-Kun Hao et al.· 1 citation
This work proposes a plug-and-play fix to automatically generate scene-specific, task-specific, adaptively updating abstractions on top of existing planning and control stacks, and shows that LENS improves classical planning, model-based control, and a vision-language-action model, across a diverse set of highly clutte...
