arXiv subjects

Anujith Muraleedharan

Publications and source records attributed to Anujith Muraleedharan.

3 recordsLinked to original sources

arXiv2026

PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization

Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this progress, recent methods attempt to transfer such models for character animation and real robot control by applying a Whole-Body Controller (WBC) that converts diffusion-generated motions into executable trajectories. While WBC trajectories become compliant with physics, they may expose substantial deviations from original motion. To address this issue, we here propose PhysMoDPO, a Direct Preference Optimization framework. Unlike prior work that relies on hand-crafted physics-aware heuristics such as foot-sliding penalties, we integrate WBC into our training pipeline and optimize diffusion model such that the output of WBC becomes compliant both with physics and original text instructions. To train PhysMoDPO we deploy physics-based and task-specific rewards and use them to assign preference to synthesized trajectories. Our extensive experiments on text-to-motion and spatial control tasks demonstrate consistent improvements of PhysMoDPO in both physical realism and task-related metrics on simulated robots. Moreover, we demonstrate that PhysMoDPO results in significant improvements when applied to zero-shot motion transfer in simulation and for real-world deployment on a G1 humanoid robot.

cs.LG↗

arXiv2025

U-LAG: Uncertainty-Aware, Lag-Adaptive Goal Retargeting for Robotic Manipulation

Robots manipulating in changing environments must act on percepts that are late, noisy, or stale. We present U-LAG, a mid-execution goal-retargeting layer that leaves the low-level controller unchanged while re-aiming task goals (pre-contact, contact, post) as new observations arrive. Unlike motion retargeting or generic visual servoing, U-LAG treats in-flight goal re-aiming as a first-class, pluggable module between perception and control. Our main technical contribution is UAR-PF, an uncertainty-aware retargeter that maintains a distribution over object pose under sensing lag and selects goals that maximize expected progress. We instantiate a reproducible Shift x Lag stress test in PyBullet/PandaGym for pick, push, stacking, and peg insertion, where the object undergoes abrupt in-plane shifts while synthetic perception lag is injected during approach. Across 0-10 cm shifts and 0-400 ms lags, UAR-PF and ICP degrade gracefully relative to a no-retarget baseline, achieving higher success with modest end-effector travel and fewer aborts; simple operational safeguards further improve stability. Contributions: (1) UAR-PF for lag-adaptive, uncertainty-aware goal retargeting; (2) a pluggable retargeting interface; and (3) a reproducible Shift x Lag benchmark with evaluation on pick, push, stacking, and peg insertion.

cs.RO↗

arXiv2025

Selective Progress-Aware Querying for Human-in-the-Loop Reinforcement Learning

Human feedback can greatly accelerate robot learning, but in real-world settings, such feedback is costly and limited. Existing human-in-the-loop reinforcement learning (HiL-RL) methods often assume abundant feedback, limiting their practicality for physical robot deployment. In this work, we introduce SPARQ, a progress-aware query policy that requests feedback only when learning stagnates or worsens, thereby reducing unnecessary oracle calls. We evaluate SPARQ on a simulated UR5 cube-picking task in PyBullet, comparing against three baselines: no feedback, random querying, and always querying. Our experiments show that SPARQ achieves near-perfect task success, matching the performance of always querying while consuming about half the feedback budget. It also provides more stable and efficient learning than random querying, and significantly improves over training without feedback. These findings suggest that selective, progress-based query strategies can make HiL-RL more efficient and scalable for robots operating under realistic human effort constraints.

cs.RO↗