arXiv subjects

Shugao Liu

Publications and source records attributed to Shugao Liu.

3 recordsLinked to original sources

arXiv2026

From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation

Learning robust robot policies in real-world environments requires diverse data augmentation, yet scaling real-world data collection is costly due to the need for acquiring physical assets and reconfiguring environments. Therefore, augmenting real-world scenes into simulation has become a practical augmentation for efficient learning and evaluation. We present a generative framework that establishes a generative real-to-sim mapping from real-world panoramas to high-fidelity simulation scenes, and further synthesize diverse cousin scenes via semantic and geometric editing. Combined with high-quality physics engines and realistic assets, the generated scenes support interactive manipulation tasks. Additionally, we incorporate multi-room stitching to construct consistent large-scale environments for long-horizon navigation across complex layouts. Experiments demonstrate a strong sim-to-real correlation validating our platform's fidelity, and show that extensively scaling up data generation leads to significantly better generalization to unseen scene and object variations, demonstrating the effectiveness of Digital Cousins for generalizable robot learning and evaluation.

cs.RO↗

arXiv2025

ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy

Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation. However, fine-tuning these models through supervised learning struggles to achieve robust performance due to limited, inconsistent demonstrations, especially in contact-rich environments. In this paper, we propose a reinforced fine-tuning approach for VLA models, named ConRFT, which consists of offline and online fine-tuning with a unified consistency-based training objective, to address these challenges. In the offline stage, our method integrates behavior cloning and Q-learning to effectively extract policy from a small set of demonstrations and stabilize value estimating. In the online stage, the VLA model is further fine-tuned via consistency policy, with human interventions to ensure safe exploration and high sample efficiency. We evaluate our approach on eight diverse real-world manipulation tasks. It achieves an average success rate of 96.3% within 45-90 minutes of online fine-tuning, outperforming prior supervised methods with a 144% improvement in success rate and 1.9x shorter episode length. This work highlights the potential of integrating reinforcement learning to enhance the performance of VLA models for real-world robotic applications. Videos and code are available at our project website https://cccedric.github.io/conrft/.

cs.RO↗

arXiv2024

Advancing Object Goal Navigation Through LLM-enhanced Object Affinities Transfer

Object-goal navigation requires mobile robots to efficiently locate targets with visual and spatial information, yet existing methods struggle with generalization in unseen environments. Heuristic approaches with naive metrics fail in complex layouts, while graph-based and learning-based methods suffer from environmental biases and limited generalization. Although Large Language Models (LLMs) as planners or agents offer a rich knowledge base, they are cost-inefficient and lack targeted historical experience. To address these challenges, we propose the LLM-enhanced Object Affinities Transfer (LOAT) framework, integrating LLM-derived semantics with learning-based approaches to leverage experiential object affinities for better generalization in unseen settings. LOAT employs a dual-module strategy: one module accesses LLMs' vast knowledge, and the other applies learned object semantic relationships, dynamically fusing these sources based on context. Evaluations in AI2-THOR and Habitat simulators show significant improvements in navigation success and efficiency, and real-world deployment demonstrates the zero-shot ability of LOAT to enhance object-goal navigation systems.

cs.RO↗