Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
Controllable-Continuous Color Editing in Diffusion Model via Color Mapping
In recent years, text-driven image editing has made significant progress. However, due to the inherent ambiguity and discreteness of natural language, color editing still faces challenges such as insufficient precision a…
Continuous ControlImage EditingLearning non-Markovian Dynamical Systems with Signature-based Encoders
Neural ordinary differential equations offer an effective framework for modeling dynamical systems by learning a continuous-time vector field. However, they rely on the Markovian assumption - that future states depend on…
Continuous ControlIncentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agen…
Reinforcement LearningContinuous ControlHow well can LLMs provide planning feedback in grounded environments?
Learning to plan in grounded environments typically requires carefully designed reward functions or high-quality annotated demonstrations. Recent works show that pretrained foundation models, such as large language model…
Continuous ControlDissecting Discrete Soft Actor-Critic: Limitations and Principled Alternatives
While Soft Actor-Critic (SAC) is highly effective in continuous control, its discrete counterpart (DSAC) performs poorly on challenging discrete-action domains such as Atari. Consequently, starting from DSAC, we revisit …
Continuous ControlAtari GamesSpiking Neural Networks for Continuous Control via End-to-End Model-Based Learning
Despite recent progress in training spiking neural networks (SNNs) for classification, their application to continuous motor control remains limited. Here, we demonstrate that fully spiking architectures can be trained e…
Continuous ControlFew-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large datasets, and struggle to solve long-hori…
Continuous ControlDecision MakingLearning Acrobatic Flight from Preferences
Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where objectives are difficult to formalize or…
Reinforcement LearningContinuous ControlAll-in-One Slider for Attribute Manipulation in Diffusion Models
Text-to-image (T2I) diffusion models have made significant strides in generating high-quality images. However, progressively manipulating certain attributes of generated images to meet the desired user expectations remai…
Continuous ControlA Dynamical Systems Framework for Reinforcement Learning Safety and Robustness Verification
The application of reinforcement learning to safety-critical systems is limited by the lack of formal methods for verifying the robustness and safety of learned policies. This paper introduces a novel framework that addr…
Reinforcement LearningContinuous ControlCategorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
A policy in deep reinforcement learning (RL), either deterministic or stochastic, is commonly parameterized as a Gaussian distribution alone, limiting the learned behavior to be unimodal. However, the nature of many prac…
Reinforcement LearningContinuous ControlEfficient Environment Design for Multi-Robot Navigation via Continuous Control
Multi-robot navigation and path planning in continuous state and action spaces with uncertain environments remains an open challenge. Deep Reinforcement Learning (RL) is one of the most popular paradigms for solving this…
Reinforcement LearningContinuous ControlRobot NavigationBeyond Fixed Morphologies: Learning Graph Policies with Trust Region Compensation in Variable Action Spaces
Trust region-based optimization methods have become foundational reinforcement learning algorithms that offer stability and strong empirical performance in continuous control tasks. Growing interest in scalable and reusa…
Reinforcement LearningContinuous ControlCaRoBio: 3D Cable Routing with a Bio-inspired Gripper Fingernail
The manipulation of deformable linear flexures has a wide range of applications in industry, such as cable routing in automotive manufacturing and textile production. Cable routing, as a complex multi-stage robot manipul…
Trajectory PlanningContinuous ControlRobot ManipulationValue Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
Value function initialization (VFI) is an effective way to achieve a jumpstart in reinforcement learning (RL) by leveraging value estimates from prior tasks. While this approach is well established in tabular settings, e…
Reinforcement LearningContinuous ControlEmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering
Text-to-speech (TTS) has shown great progress in recent years. However, most existing TTS systems offer only coarse and rigid emotion control, typically via discrete emotion labels or a carefully crafted and detailed emo…
Continuous ControlAttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models
Diffusion models have recently become the dominant paradigm for image generation, yet existing systems struggle to interpret and follow numeric instructions for adjusting semantic attributes. In real-world creative scena…
Reinforcement LearningContinuous ControlImage GenerationAdaptive Ensemble Aggregation for Actor-Critics
Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or task-specific hyperparameters to balance o…
Reinforcement LearningContinuous ControlAssistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks. Games have dominated RL benchmarks because they present relevant challenges, are inexpensive …
Reinforcement LearningContinuous ControlFlow Matching Policy Gradients
Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization (FPO), a simple on-policy reinforcement…
Reinforcement LearningContinuous Control