VoLoAgent uses a VLM to steer heterogeneous robot capabilities as interruptible tools for long-horizon manipulation and introduces the RoboVoLo benchmark, claiming substantial outperformance over single VLA/VLM or tool-based systems with real-robot validation.
RePLan: Robotic replanning with perception and language models
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
abstract
Advancements in large language models (LLMs) have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have also been able to generate reward functions for low-level robot actions, effectively bridging the interface between high-level planning and low-level robot control. However, the challenge remains that even with syntactically correct plans, robots can still fail to achieve their intended goals due to imperfect plans or unexpected environmental issues. To overcome this, Vision Language Models (VLMs) have shown remarkable success in tasks such as visual question answering. Leveraging the capabilities of VLMs, we present a novel framework called Robotic Replanning with Perception and Language Models (RePLan) that enables online replanning capabilities for long-horizon tasks. This framework utilizes the physical grounding provided by a VLM's understanding of the world's state to adapt robot actions when the initial plan fails to achieve the desired goal. We developed a Reasoning and Control (RC) benchmark with eight long-horizon tasks to test our approach. We find that RePLan enables a robot to successfully adapt to unforeseen obstacles while accomplishing open-ended, long-horizon goals, where baseline models cannot, and can be readily applied to real robots. Find more information at https://replan-lm.github.io/replan.github.io/
years
2026 6representative citing papers
Ego2World turns real egocentric cooking videos into hidden symbolic world graphs for evaluating belief-state planning and memory in embodied agents.
ANCHOR raises mobile manipulation success from 53.3% to 71.7% in unseen homes by binding plans to observable geometry, ensuring operable navigation endpoints, and using layered local recovery instead of global replans.
A VLM planner that adaptively inserts latent visual thoughts of future states into its reasoning trace beats language-only and prior VLM planners on long-horizon kitchen tasks, especially under tight free space.
RePO-VLA raises average adversarial success rates in VLA manipulation from 20% to 75% by using recovery-aware initialization, a progress-aware semantic value function, and value-conditioned refinement on success and corrective trajectories.
RePlan-Bot achieves state-of-the-art results on the ALFRED benchmark for embodied instruction following by integrating LLM-based auditing, commonsense map search, and ViT action correction.
citing papers explorer
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VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation
VoLoAgent uses a VLM to steer heterogeneous robot capabilities as interruptible tools for long-horizon manipulation and introduces the RoboVoLo benchmark, claiming substantial outperformance over single VLA/VLM or tool-based systems with real-robot validation.
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Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning
Ego2World turns real egocentric cooking videos into hidden symbolic world graphs for evaluating belief-state planning and memory in embodied agents.
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ANCHOR: A Physically Grounded Closed-Loop Framework for Robust Home-Service Mobile Manipulation
ANCHOR raises mobile manipulation success from 53.3% to 71.7% in unseen homes by binding plans to observable geometry, ensuring operable navigation endpoints, and using layered local recovery instead of global replans.
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APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts
A VLM planner that adaptively inserts latent visual thoughts of future states into its reasoning trace beats language-only and prior VLM planners on long-horizon kitchen tasks, especially under tight free space.
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RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models
RePO-VLA raises average adversarial success rates in VLA manipulation from 20% to 75% by using recovery-aware initialization, a progress-aware semantic value function, and value-conditioned refinement on success and corrective trajectories.
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RePlan-Bot: Multi-Level Replanning for Embodied Instruction Following
RePlan-Bot achieves state-of-the-art results on the ALFRED benchmark for embodied instruction following by integrating LLM-based auditing, commonsense map search, and ViT action correction.