LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction. To address this, we propose a benchmark system for automatically quantifying performance of task planning for home-service embodied agents. Task planners are tested on two pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. We expect that the proposed benchmark tool would accelerate the development of language-oriented task planners.
Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 33 of its 55 references that are in the map. First posted on arXiv 13 Feb 2024.
Builds on
33 of 55 references.
Closest ancestors · 2
Same direction, newest first
- 2023Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents3 May
- 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents18 Jan
Other references · 31
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
- 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
- 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,007
- 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
- 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172
- 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
19 more
- 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,057
- 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
- 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
- 2018VirtualHome: Simulating Household Activities via Programscited by 411
- 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
- 2022Pre-Trained Language Models for Interactive Decision-Makingcited by 220
- 2019Search on the Replay Buffer: Bridging Planning and Reinforcement Learningcited by 188
- 2022Open-vocabulary Queryable Scene Representations for Real World Planningcited by 159
- 2018PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planningcited by 147
- 2020Watch-And-Help: A Challenge for Social Perception and Human-AI Collaborationcited by 131
- 2019Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generationcited by 83
- 2022Learning Neuro-Symbolic Skills for Bilevel Planningcited by 55
- 2022On Grounded Planning for Embodied Tasks with Language Modelscited by 33
- 2019Regression Planning Networkscited by 32
- 2020Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planningcited by 26
- 2021Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoningcited by 25
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Citing papers per year: 2024: 7; 2025: 22; 2026: 22.
51 papers.
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- 2026AsgardBench -- Evaluating Visually Grounded Interactive Planning Under Minimal Feedback16 Mar 2026
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