Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the image-conditioning signal, they often requ…
Continuous ControlSampling-Based Safe Reinforcement Learning
Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based Safe Reinforcement Learning (SBSRL), a model-based RL alg…
Reinforcement LearningContinuous ControlImplicit Action Chunking for Smooth Continuous Control
Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deployment. Explicit action chunking addresses this by predicting fixed-horiz…
Reinforcement LearningContinuous ControlAutonomous DrivingBAPR: Bayesian amnesic piecewise-robust reinforcement learning for non-stationary continuous control
Real-world control systems frequently operate under \emph{piecewise stationary} conditions, where dynamics remain stable for extended periods before undergoing abrupt regime changes. Standard robust RL methods face a fun…
Reinforcement LearningContinuous ControlChange DetectionEfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is a planner that aims to find an action s…
Reinforcement LearningContinuous ControlAgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech
While existing text-to-speech (TTS) models exhibit high expressiveness, fine-grained control over composite instructions remains challenging due to the structural mismatch between discrete textual intents and continuous …
Continuous ControlR2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
For reinforcement learning in data-scarce domains like real-world robotics, intensive data reuse enhances efficiency but induces overfitting. While prior works focus on critic bias, representation-level instability in Se…
Reinforcement LearningContinuous ControlAugmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs
We study policy optimization for infinite-horizon, discounted constrained Markov decision processes (CMDPs). While existing theoretical guarantees typically hold for the mixture policy, deploying such a policy is computa…
Continuous ControlDebiased Model-based Representations for Sample-efficient Continuous Control
Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream off-policy actor-critic learning. It implicitly combines the advanta…
Continuous ControlHolder Policy Optimisation
Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mapping these trajectory-level advantages to policy updates requires aggr…
Continuous ControlSparse LearningTransferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
Random delays weaken the temporal correspondence between actions and subsequent state feedback, making it difficult for agents to identify the true propagation process of action effects. In cross-task scenarios, changes …
Reinforcement LearningContinuous ControlDiscrete Flow Matching for Offline-to-Online Reinforcement Learning
Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed for continuous control. Meanwhile, generative policies usually rely…
Reinforcement LearningContinuous ControlMindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
Autonomous driving has progressed from modular pipelines toward end-to-end unification, and Vision-Language-Action (VLA) models are a natural extension of this journey beyond Vision-to-Action (VA). In practice, driving V…
Continuous ControlAutonomous DrivingLatent Geometry Beyond Search: Amortizing Planning in World Models
Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging. This raises a central question: when does a lea…
Continuous ControlSOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data
Incorporating prior data into online reinforcement learning accelerates training but typically forces a difficult trade-off between high computational costs and long, multi-stage training pipelines. While fixed-length st…
Computational EfficiencyReinforcement LearningContinuous ControlEntropy-Regularized Adjoint Matching for Offline Reinforcement Learning
Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture complex, multi-modal behaviors. While Q-learning with Adjoint Matching (QAM) sta…
Reinforcement LearningContinuous ControlSoft Deterministic Policy Gradient with Gaussian Smoothing
Deterministic policy gradient (DPG) is widely utilized for continuous control; however, it inherently relies on the differentiability of the critic with respect to the action during policy updates. This assumption is vio…
Reinforcement LearningContinuous ControlA Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment
Large language model (LLM) alignment via reinforcement learning from human preferences (RLHF) suffers from unstable policy updates, ambiguous gradient directions, poor interpretability, and high gradient variance in main…
General Reinforcement LearningContinuous ControlDream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning. Hybrid appro…
Reinforcement LearningContinuous ControlNeural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning
A key capability of intelligent agents is operating under partial observability: reasoning and acting effectively despite missing or incomplete state observations. While recurrent (memory-based) policies learned via rein…
Reinforcement LearningContinuous Control