5 papers
ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation
Zixuan Chen, Chongkai Gao, Lin Shao +3
One-shot imitation learning (OSIL) offers a promising way to teach robots new skills without large-scale data collection. However, current OSIL methods are primarily limited to sho…
R2Q: Towards Robust 2-Bit Large Language Models via Residual Refinement Quantization
Jiayi Chen, Jieqi Shi, Jing Huo +1
The rapid progress of Large Language Models (LLMs) has brought substantial computational and memory demands, spurring the adoption of low-bit quantization. While 8-bit and 4-bit fo…
RoboHiMan: A Hierarchical Evaluation Paradigm for Compositional Generalization in Long-Horizon Manipulation
Yangtao Chen, Zixuan Chen, Nga Teng Chan +6
Enabling robots to flexibly schedule and compose learned skills for novel long-horizon manipulation under diverse perturbations remains a core challenge. Early explorations with en…
SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas
Hongyu Ding, Xinyue Liang, Yudong Fang +7
In this paper, we propose SEA, a novel approach for active robot exploration through semantic map prediction and a reinforcement learning-based hierarchical exploration policy. Unl…
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving
Muleilan Pei, Jiayao Shan, Peiliang Li +4
Online scene perception and topology reasoning are critical for autonomous vehicles to understand their driving environments, particularly for mapless driving systems that endeavor…