LongAct benchmark evaluates long-horizon household task execution from free-form instructions; HoloMind agent raises performance but top VLMs still reach only 59% goal completion and 16% full-task success.
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SayPlan: Grounding large language models using 3d scene graphs for scalable robot task planning
19 Pith papers cite this work. Polarity classification is still indexing.
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ANCHOR creates synthetic noise labels via recommender-in-the-loop LLM agents and trains a parametric recognizer on them to perform supervised denoising of implicit feedback.
KITE is a training-free method that uses keyframe-indexed tokenized evidence including BEV schematics to enhance VLM performance on robot failure detection, identification, localization, explanation, and correction.
ST-BiBench reveals a coordination paradox in which MLLMs show strong high-level strategic reasoning yet fail at fine-grained 16-dimensional bimanual action synthesis and multi-stream fusion.
BridgeEQA creates a new benchmark and EMVR method for embodied agents to perform question answering on real-world bridge inspections using egocentric images and professional reports.
LLM+P lets LLMs solve planning problems optimally by converting them to PDDL for classical planners and back to natural language.
BDDL-based symbolic verification and GroupAdapt scheduling let an 8B RL planner reach 97.3 Strict-Pass on BEHAVIOR-1000 with 79 percent shorter plans than prior baselines.
ASCII rendering of visual states enables fine-tuned text-only LLMs to serve as VLA controllers that identify objects and generate feasible action sequences in 2D manipulation benchmarks in simulation and on hardware.
Experiments reveal that topological cues robustly support LLM navigation planning while incorrect semantic cues derail it, with linguistic format effects varying by model size and compression.
VEOcc is a voxel-based online semantic occupancy prediction method using recursive assimilation and three update modules (TLA, RCM, CSU) that reports new SOTA results on Occ-ScanNet and EmbodiedOcc-ScanNet.
RGB-only active 3D scene graph generation unifies perception and planning to achieve depth-baseline parity and more than double object detection in active indoor exploration.
Fixed external cameras as Common Prior Maps boost initial object recall in 3D scene graph generation by up to 79% and improve active exploration efficiency.
LoHo-Manip enables robust long-horizon robot manipulation by using a receding-horizon VLM manager to output progress-aware subtask sequences and 2D visual traces that condition a VLA executor for automatic replanning.
InternVLA-M1 uses spatially guided pre-training on 2.3M examples followed by action post-training to deliver up to 17% gains on robot manipulation benchmarks and 20.6% on unseen objects.
AlloSpatial adds structured allocentric priors and a harness for tool-use and arbitration to improve spatial reasoning in foundation models, with 5-18% gains on VSI-Bench and MindCube in training-free settings and further gains after RL internalization.
A vision-language model outputs dual heatmaps for navigation affordance and facing to ground semantic instructions into executable free space, achieving higher affordance rates than waypoint regression across simulated robot embodiments.
TaskGround introduces a Ground-Infer-Execute framework for full-scene household reasoning that improves success rates on the FullHome benchmark and enables compact models to match larger ones at up to 18x lower token cost.
Digital twin representations from vision foundation models enable LLM-based planning for robust peg transfer and gauze retrieval on the dVRK surgical platform with claimed generalizability.
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
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LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
LLM+P lets LLMs solve planning problems optimally by converting them to PDDL for classical planners and back to natural language.
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The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.