Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
ArchitectHead: Continuous Level of Detail Control for 3D Gaussian Head Avatars
3D Gaussian Splatting (3DGS) has enabled photorealistic and real-time rendering of 3D head avatars. Existing 3DGS-based avatars typically rely on tens of thousands of 3D Gaussian points (Gaussians), with the number of Ga…
Continuous ControlAutomaton Constrained Q-Learning
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigms are fundamentally limited in these set…
Reinforcement LearningContinuous ControlSAEdit: Token-level control for continuous image editing via Sparse AutoEncoder
Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two properties are especially desirable: disenta…
Continuous ControlImage EditingA KL-regularization Framework for Learning to Plan with Adaptive Priors
Effective exploration remains a central challenge in model-based reinforcement learning (MBRL), particularly in high-dimensional continuous control tasks where sample efficiency is crucial. A prominent line of recent wor…
Reinforcement LearningContinuous ControlSingle-Rod Brachiation Robot: Mechatronic Control Design and Validation of Prejump Phases
A complete mechatronic design of a minimal configuration brachiation robot is presented. The robot consists of a single rigid rod with gripper mechanisms attached to both ends. The grippers are used to hang the robot on …
Continuous ControlPolicy Gradient Guidance Enables Test Time Control
We introduce Policy Gradient Guidance (PGG), a simple extension of classifier-free guidance from diffusion models to classical policy gradient methods. PGG augments the policy gradient with an unconditional branch and in…
Reinforcement LearningContinuous ControlA Forensic Analysis of Synthetic Data in RL: Diagnosing and Solving Algorithmic Failures in Model-Based Policy Optimization
Synthetic data is central to data-efficient Dyna-style model-based reinforcement learning, but it can also degrade performance. We study this failure in Model-Based Policy Optimization (MBPO), which performs actor-critic…
Reinforcement LearningContinuous ControlOpenAI GymDensity-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets
Offline reinforcement learning (RL) enables policy optimization from fixed datasets, making it suitable for safety-critical applications where online exploration is infeasible. However, these datasets are often contamina…
Reinforcement LearningContinuous ControlOffline RLSAC Flow: Sample-Efficient Reinforcement Learning of Flow-Based Policies via Velocity-Reparameterized Sequential Modeling
Training expressive flow-based policies with off-policy reinforcement learning is notoriously unstable due to gradient pathologies in the multi-step action sampling process. We trace this instability to a fundamental con…
Reinforcement LearningContinuous ControlEfficient On-Policy Reinforcement Learning via Exploration of Sparse Parameter Space
Policy-gradient methods such as Proximal Policy Optimization (PPO) are typically updated along a single stochastic gradient direction, leaving the rich local structure of the parameter space unexplored. Previous work has…
Reinforcement LearningContinuous ControlNoise-Guided Transport for Imitation Learning
We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretraining or high-capacity architectures can …
Continuous ControlFine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning
Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by …
Reinforcement LearningContinuous ControlXQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Sample efficiency is a central property of effective deep reinforcement learning algorithms. Recent work has improved this through added complexity, such as larger models, exotic network architectures, and more complex a…
Reinforcement LearningContinuous ControlCaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision-making on neuromorphic hardware by mimicking the event-driven dynamics of biological neurons. However, the discrete and non-differentiable na…
Reinforcement LearningContinuous ControlSAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks
Successfully solving long-horizon manipulation tasks remains a fundamental challenge. These tasks involve extended action sequences and complex object interactions, presenting a critical gap between high-level symbolic p…
Continuous ControlImage InpaintingImage EditingFrom Parameters to Behaviors: Unsupervised Compression of the Policy Space
Despite its recent successes, Deep Reinforcement Learning (DRL) is notoriously sample-inefficient. We argue that this inefficiency stems from the standard practice of optimizing policies directly in the high-dimensional …
Reinforcement LearningContinuous ControlPersonality Vector: Modulating Personality of Large Language Models by Model Merging
Driven by the demand for personalized AI systems, there is growing interest in aligning the behavior of large language models (LLMs) with human traits such as personality. Previous attempts to induce personality in LLMs …
Continuous ControlModeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain. For reliable deployment, however, learned dynamics should respect physical structure and l…
Continuous ControlText Slider: Efficient and Plug-and-Play Continuous Concept Control for Image/Video Synthesis via LoRA Adapters
Recent advances in diffusion models have significantly improved image and video synthesis. In addition, several concept control methods have been proposed to enable fine-grained, continuous, and flexible control over fre…
Continuous ControlExplainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems
We present an explainable AI-enhanced supervisory control framework for multi-agent robotics that combines (i) a timed-automata supervisor for safe, auditable mode switching, (ii) robust continuous control (Lyapunov-base…
Continuous Control