Youwei Yu

Youwei Yu, Jionghao Wang, Zhengming Yu et al.

Designing learnable information-theoretic objectives for robot exploration remains challenging. Such objectives aim to guide exploration toward data that reduces uncertainty in model parameters, yet it is often unclear what information the collected data can actually reveal. Although reinforcement learning (RL) can optimize a given objective, constructing objectives that reflect parametric learnability is difficult in high-dimensional robotic systems. Many parameter directions are weakly observable or unidentifiable, and even when identifiable directions are selected, omitted directions can still influence exploration and distort information measures. To address this challenge, we propose Quasi-Optimal Experimental Design (Q{\footnotesize OED}), an adaptive information objective grounded in optimal experimental design. Q{\footnotesize OED} (i) performs eigenspace analysis of the Fisher information matrix to identify an observable subspace and select identifiable parameter directions, and (ii) modifies the exploration objective to emphasize these directions while suppressing nuisance effects from non-critical parameters. Under bounded nuisance influence and limited coupling between critical and nuisance directions, Q{\footnotesize OED} provides a constant-factor approximation to the ideal information objective that explores all parameters. We evaluate Q{\footnotesize OED} on simulated and real-world navigation and manipulation tasks, where identifiable-direction selection and nuisance suppression yield performance improvements of \SI{35.23}{\percent} and \SI{21.98}{\percent}, respectively. When integrated as an exploration objective in model-based policy optimization, Q{\footnotesize OED} further improves policy performance over established RL baselines.

Youwei Yu, Junhong Xu, Lantao Liu

Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured terrains. The effectiveness of these methods hinges on two essential elements: (1) the use of massively parallel physics simulations to expedite policy training, and (2) an environment generator tasked with crafting sufficiently challenging yet attainable terrains to facilitate continuous policy improvement. Existing methods of environment generation often rely on heuristics constrained by a set of parameters, limiting the diversity and realism. In this work, we introduce the Adaptive Diffusion Terrain Generator (ADTG), a novel method that leverages Denoising Diffusion Probabilistic Models to dynamically expand existing training environments by adding more diverse and complex terrains adaptive to the current policy. ADTG guides the diffusion model's generation process through initial noise optimization, blending noise-corrupted terrains from existing training environments weighted by the policy's performance in each corresponding environment. By manipulating the noise corruption level, ADTG seamlessly transitions between generating similar terrains for policy fine-tuning and novel ones to expand training diversity. Our experiments show that the policy trained by ADTG outperforms both procedural generated and natural environments, along with popular navigation methods.

Youwei Yu, Lantao Liu

Deep reinforcement learning can seamlessly transfer agile locomotion and navigation skills from the simulator to real world. However, bridging the sim-to-real gap with domain randomization or adversarial methods often demands expert physics knowledge to ensure policy robustness. Even so, cutting-edge simulators may fall short of capturing every real-world detail, and the reconstructed environment may introduce errors due to various perception uncertainties. To address these challenges, we propose Neural Fidelity Calibration (NFC), a novel framework that employs conditional score-based diffusion models to calibrate simulator physical coefficients and residual fidelity domains online during robot execution. Specifically, the residual fidelity reflects the simulation model shift relative to the real-world dynamics and captures the uncertainty of the perceived environment, enabling us to sample realistic environments under the inferred distribution for policy fine-tuning. Our framework is informative and adaptive in three key ways: (a) we fine-tune the pretrained policy only under anomalous scenarios, (b) we build sequential NFC online with the pretrained NFC's proposal prior, reducing the diffusion model's training burden, and (c) when NFC uncertainty is high and may degrade policy improvement, we leverage optimistic exploration to enable hallucinated policy optimization. Our framework achieves superior simulator calibration precision compared to state-of-the-art methods across diverse robots with high-dimensional parametric spaces. We study the critical contribution of residual fidelity to policy improvement in simulation and real-world experiments. Notably, our approach demonstrates robust robot navigation under challenging real-world conditions, such as a broken wheel axle on snowy surfaces.