Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
Is Your Conditional Diffusion Model Actually Denoising?
We study the inductive biases of diffusion models with a conditioning-variable, which have seen widespread application as both text-conditioned generative image models and observation-conditioned continuous control polic…
Continuous ControlNeuro-Symbolic Control with Large Language Models for Language-Guided Spatial Tasks
Although large language models (LLMs) have recently become effective tools for language-conditioned control in embodied systems, instability, slow convergence, and hallucinated actions continue to limit their direct appl…
Reinforcement LearningContinuous ControlTiny Recursive Control: Iterative Reasoning for Efficient Optimal Control
Neural network controllers increasingly demand millions of parameters, and language model approaches push into the billions. For embedded aerospace systems with strict power and latency constraints, this scaling is prohi…
Continuous ControlPrismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems
Model-based planning in robotic domains is challenged by the hybrid nature of physical dynamics, where continuous motion is punctuated by discrete events such as contacts and impacts. Conventional latent world models typ…
Continuous ControlQuantum Decision Transformers (QDT): Synergistic Entanglement and Interference for Offline Reinforcement Learning
Offline reinforcement learning enables policy learning from pre-collected datasets without environment interaction, but existing Decision Transformer (DT) architectures struggle with long-horizon credit assignment and co…
Reinforcement LearningContinuous ControlNeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
Standard diffusion corrupts data using Gaussian noise whose Fourier coefficients have random magnitudes and random phases. While effective for unconditional or text-to-image generation, corrupting phase components destro…
Image-to-Image TranslationText-to-Image GenerationContinuous ControlVideo GenerationEnhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay
Background: Deep Deterministic Policy Gradient-based reinforcement learning algorithms utilize Actor-Critic architectures, where both networks are typically trained using identical batches of replayed transitions. Howeve…
Reinforcement LearningContinuous ControlOpenAI GymDifferentiable Weightless Controllers: Learning Logic Circuits for Continuous Control
Controlling autonomous systems under real-world conditions often requires policies that can be evaluated with low latency and minimal energy consumption. Unfortunately, these conditions are at odds with the use of high-p…
Continuous ControlSigma: The Key for Vision-Language-Action Models toward Telepathic Alignment
To address a fundamental limitation in cognitive systems, namely the absence of a time-updatable mediating thought space between semantics and continuous control, this work constructs and trains a vision-language-action …
Continuous ControlPartially Equivariant Reinforcement Learning in Symmetry-Breaking Environments
Group symmetries provide a powerful inductive bias for reinforcement learning (RL), enabling efficient generalization across symmetric states and actions via group-invariant Markov Decision Processes (MDPs). However, rea…
Reinforcement LearningContinuous ControlHardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control
Robotic continuous control tasks impose stringent demands on the energy efficiency and latency of computing architectures due to their high-dimensional state spaces and real-time interaction requirements. Conventional el…
Reinforcement LearningContinuous ControlLearning Massively Multitask World Models for Continuous Control
General-purpose control demands agents that act across many tasks and embodiments, yet research on reinforcement learning (RL) for continuous control remains dominated by single-task or offline regimes, reinforcing a vie…
Reinforcement LearningContinuous ControlMobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots
Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge high-level semantic reasoning and low-le…
Reinforcement LearningContinuous ControlDeep Gaussian Process Proximal Policy Optimization
Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural networks have enabled breakthroughs in …
Reinforcement LearningContinuous ControlGaussian ProcessesHybrid Differential Reward: Combining Temporal Difference and Action Gradients for Efficient Multi-Agent Reinforcement Learning in Cooperative Driving
In multi-vehicle cooperative driving tasks involving high-frequency continuous control, traditional state-based reward functions suffer from the issue of vanishing reward differences. This phenomenon results in a low sig…
Multi-agent Reinforcement LearningContinuous ControlMitigating Estimation Bias with Representation Learning in TD Error-Driven Regularization
Deterministic policy gradient algorithms for continuous control suffer from value estimation biases that degrade performance. While double critics reduce such biases, the exploration potential of double actors remains un…
Representation LearningContinuous ControlZ-Merge: Multi-Agent Reinforcement Learning for On-Ramp Merging with Zone-Specific V2X Traffic Information
Ramp merging is a critical and challenging task for autonomous vehicles (AVs), particularly in mixed traffic environments with human-driven vehicles (HVs). Existing approaches typically rely on either lane-changing or in…
Multi-agent Reinforcement LearningAutonomous VehiclesContinuous ControlBoosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design
Reinforcement Learning is a mature technology, often suggested as a potential route towards Artificial General Intelligence, with the ambitious goal of replicating the wide range of abilities found in natural and artific…
Reinforcement LearningContinuous ControlAtari GamesUnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity
The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity remains limited. Users often need to refine results manually - by adding mo…
Self-Supervised LearningContinuous ControlVideo SegmentationDynamic Weight Adaptation in Spiking Neural Networks Inspired by Biological Homeostasis
Homeostatic mechanisms play a crucial role in maintaining optimal functionality within the neural circuits of the brain. By regulating physiological and biochemical processes, these mechanisms ensure the stability of an …
Continuous Control