arXiv subjects

Xinchen Yao

Publications and source records attributed to Xinchen Yao.

4 recordsLinked to original sources

arXiv2026

DynaForcing: Overcoming Dynamic Collapse in Self-Forcing Distillation for Streaming Avatar Generation

Audio-driven avatar generation requires realistic lip-sync, expressive motion, and real-time streaming. Recent work achieves the latter via self-forcing with Distribution Matching Distillation (DMD), but this paradigm suffers from a critical failure that has not been systematically characterized: dynamic collapse, where the student model converges to a near-static optimum with high perceptual quality but severely suppressed temporal dynamics. We trace this to two causes: the reverse KL objective in DMD, which biases toward low-motion modes, and unanchored self-conditioning, which creates a feedback loop that amplifies collapse. This is especially harmful for avatars, where even subtle motion loss breaks lip-sync and expression. To address this, we propose DynaForcing, a training framework with three complementary strategies applied at different levels. Specifically, Hybrid Forcing anchors rollouts to ground-truth dynamics at the data level to break the feedback loop. Dynamics-Aware Reward Regularization introduces explicit motion rewards via the RL interpretation of DMD to counteract the reverse KL bias at the loss level. Reference Perturbation perturbs reference images to decouple identity from static details, forcing the model to rely on audio for motion at the conditioning level. We further introduce computation graph pruning and gradient replay, reducing the GPU footprint of self-forcing by over an order of magnitude. Experiments show that DynaForcing recovers dynamics to teacher-comparable levels (Dyn-Deg: 0.31 -> 0.73, Sync-C: 7.03 -> 7.68) while improving visual quality, resolving the quality-dynamics trade-off throughout training without early stopping.

cs.CV↗

arXiv2026

World Translation: Minimizing Sim-to-Real Gap with Backward Dynamics Extraction and Unpaired Domain Translation

The gap between simulation and reality remains a fundamental challenge in deploying simulation-trained robotic policies in the real world. Real-to-sim methods narrow this gap from the real side, learning transition dynamics from real data to build a more realistic digital world. Learned dynamics models are their dominant instance. Such methods, however, face a partial observability problem: the same observation may branch to different transitions due to unobservable factors. Existing methods assume these factors can be recovered from observation history. However, this may fail whenever observation history is uninformative, such as a sudden contact event with no prior warning. To address this limitation, we propose \textit{World Translation}, which exploits a complementary strength of simulators and learned dynamics. Simulators are deterministic but physically imperfect, while learned models are accurate but underdetermined under partial observability. Rather than predicting transitions forward from history, we extract the unobservable dynamics information backward from an observed transition, then translate this feature across simulation and reality as an unpaired domain-translation problem that preserves dynamics content while transferring domain style. Experiments across humanoid, quadruped, and manipulator platforms show that our method achieves more accurate dynamics modeling than baselines, with the largest gains when unobservable factors cannot be recovered from observation history. Real-robot deployment on Go2 quadruped confirms improved policy transfer.

cs.RO↗

arXiv2026

Where-to-Learn: Analytical Policy Gradient Directed Exploration for On-Policy Robotic Reinforcement Learning

On-policy reinforcement learning (RL) algorithms have demonstrated great potential in robotic control, where effective exploration is crucial for efficient and high-quality policy learning. However, how to encourage the agent to explore the better trajectories efficiently remains a challenge. Most existing methods incentivize exploration by maximizing the policy entropy or encouraging novel state visiting regardless of the potential state value. We propose a new form of directed exploration that uses analytical policy gradients from a differentiable dynamics model to inject task-aware, physics-guided guidance, thereby steering the agent towards high-reward regions for accelerated and more effective policy learning.

cs.RO↗

arXiv2025

Symmetric Sliding-Mode Control of Grid-Forming Inverters With Precision Region Under AC and DC Sides Varying

Voltage regulation under conventional grid-forming controllers is tightly coupled to power sharing and dc-link dynamics. Consequently, its tracking accuracy deteriorates during grid faults, sudden power sharing changes, or dc-bus voltage varying. To address this issue, a symmetric sliding-mode control (SSMC) method is developed and its voltage precision region is derived. It illustrates how much ac-side power dynamics and dc-link voltage varying can be decoupled from the voltage regulation task, which helps predict when an abnormal entangling appears. While conventional sliding-mode controls address voltage-tracking error through complex sliding surface designs, repetitive correction techniques or special reaching laws, this work identifies that the error at power-line frequency primarily stem from the asymmetry property of inverters with the delay effect and the computational inaccuracy. Guided by this insight, an asymmetry compensation structure is proposed, which avoids added design complexity and directly mitigates voltage tracking error. Furthermore, the control design is supported by a physical and quantitative explanation, aiding in parameter tuning. Simulation and experimental results demonstrate that the proposed method achieves faster tracking responses while maintaining robust and more accurate tracking under both dc-link voltage and ac-side current variations. Conventional grid-forming and classical sliding-mode controllers, which handle these variations separately, cannot match this combined speed and robustness. Furthermore, the voltage precision region is explicitly verified.

eess.SY↗