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I2O2I-Nav: A Benchmark with a Modular Agent for Long-Horizon Indoor-to-Outdoor-to-Indoor Navigation
Zhengjie Shu, Wanyu Li, Di Xiao +11
Service and delivery robots often need to complete long-horizon navigation missions spanning indoor and outdoor environments. Existing embodied navigation benchmarks primarily eval…
World4Scorer: Outcome-Grounded World Modeling for Autonomous Driving
Jieyuan Pei, Meiyi Lu, Sining Ang +12
Autonomous driving requires choosing a safe and efficient plan as surrounding traffic evolves. Generate-and-select planners propose multiple trajectories and score them for executi…
Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
Ruilin Chen, Jingkai Jia, Tong Yang +8
Tactile-enhanced vision-language-action (VLA) policies have been introduced for contact-rich manipulation, where critical interaction states are often hidden from vision. Future ta…
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
Lianghao Luo, Xizhou Bu, Ruyan Liu +5
Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or even failed trajectories. Dis…
LongBench: Evaluating Robotic Manipulation Policies on Real-World Long-Horizon Tasks
Xueyao Chen, Jingkai Jia, Tong Yang +3
Robotic manipulation policies often degrade over extended horizons, yet existing benchmarks provide limited insight into why such failures occur. Most prior benchmarks are either s…
Diverse Skill Discovery for Quadruped Robots via Unsupervised Learning
Ruopeng Cui, Yifei Bi, Haojie Luo +1
Reinforcement learning necessitates meticulous reward shaping by specialists to elicit target behaviors, while imitation learning relies on costly task-specific data. In contrast,…
Real Garment Benchmark (RGBench): A Comprehensive Benchmark for Robotic Garment Manipulation featuring a High-Fidelity Scalable Simulator
Wenkang Hu, Xincheng Tang, Yanzhi E +5
While there has been significant progress to use simulated data to learn robotic manipulation of rigid objects, applying its success to deformable objects has been hindered by the…
LAOF: Robust Latent Action Learning with Optical Flow Constraints
Xizhou Bu, Jiexi Lyu, Fulei Sun +3
Learning latent actions from large-scale videos is crucial for the pre-training of scalable embodied foundation models, yet existing methods often struggle with action-irrelevant d…
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Xiaoxiao Long, Qingrui Zhao, Kaiwen Zhang +15
The pursuit of artificial general intelligence (AGI) has placed embodied intelligence at the forefront of robotics research. Embodied intelligence focuses on agents capable of perc…