https://github.com/rstrudel/rlbc
공식 코드Learning to combine primitive skills: A step towards versatile robotic manipulation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Learning to combine primitive skills: A step towards versatile robotic manipulation
Control What You Can: Intrinsically Motivated Task-Planning Agent
A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents
Agent S: An Open Agentic Framework that Uses Computers Like a Human
Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems
New Interaction Paradigm for Complex EDA Software Leveraging GPT
Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation
Sequential Planning in Large Partially Observable Environments guided by LLMs
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
SORNet: Spatial Object-Centric Representations for Sequential Manipulation
Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning
Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Planning with Multi-Constraints via Collaborative Language Agents
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.