This paper presents ABot-C0, a motion-control system for quadruped robots, which includes a scalable multi-source motion-data pipeline, robust policy learning, and a unified deployment stack for real-…

The paper introduces CA-AC-MPC, a CUDA-accelerated variant of Actor-Critic Model Predictive Control, which significantly reduces the training and inference latency of AC-MPC while maintaining state-of…

The paper proposes FT-WBC, a fault-tolerant loco-manipulation framework for legged manipulators using a decoupled policy architecture, a Fault Estimator, and a Posture Adaptation Module for fault-awar…

This paper proposes VOP-Nav, a novel navigation system for quadruped robots that combines the geometric safety of Velocity Obstacles with the agile adaptability of end-to-end learning.

The paper proposes a novel framework to visualize and uncover latent, structured motion phases in deep reinforcement learning locomotion policies by augmenting state observations with action and next-…

The paper introduces a Variational Encrypted Model Predictive Control (VEMPC) protocol that enables online MPC execution using only encrypted polynomial operations, eliminating the need for intermedia…

This paper studies how to scale robust robot policies by expanding physical domains in a recoverable way.

This paper presents a method to equip existing motion planning algorithms with probabilistic task-completion guarantees on systems with unknown dynamics using a planner-agnostic constraint-tightening…

TempoVLA is a novel Vision-Language-Action model that enables controllable execution speed for robot manipulation by explicitly conditioning the policy on the desired speed.

This paper presents OrchardBench, a physically-grounded, GPU-parallel simulation of apple-orchard trees for robotic tree-fruit harvesting research.

The paper introduces Q-ALIGN DT, a novel framework that improves conditioned sequence models by enforcing alignment between the input return-to-go (RTG) signal and the output policy's expected Q-value…

Proposed an asynchronous block coordinate descent algorithm for distributed trajectory estimation in robotics, reducing communications by up to 96.9% and achieving exponential convergence.

The paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework that enables stable, scalable consensus control for large swarms of quadcopters using only local neighbo…

The paper proposes a robust sim-to-sim-to-real DRL approach to enable double-Ackermann robots to achieve full pose control despite significant actuation uncertainties and discrepancies between simulat…

This paper introduces Retriever, an asynchronous decision model and runtime system for building long-horizon robot agents with explicit clock and input-consumption semantics.

The paper proposes an iCEM+TL framework that combines the Sample-efficient Cross-Entropy Method with Transfer Learning and Reward Redesign to improve robotic motion planning for complex tasks like sta…