Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,431편 · 필터 해제
Event-Adaptive Motion Planning with Distilled Vision-Language Model in Safety-Critical Situations
Robot navigation in safety-critical scenarios faces significant challenges from unforeseen semantic events, where collisions arise primarily from the unpredictable behaviors of dynamic agents rather than unseen objects. …
Continuous ControlRobot NavigationMotion PlanningBridging Spherical Black-Box Optimizers
When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative. While Evolution Strategies (ES), Consensus-Based Optimization (CBO), Optimization via Integration (OVI), and…
Continuous ControlHolistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning
The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre-training. Online Data Mixing (ODM), the technique of adaptively adjusti…
Reinforcement LearningContinuous ControlCausal Reward World Models: Zero-shot Reward Design for Automated Skill Generation
Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis. However, existing approaches based on large language models (LLMs) remain …
Reinforcement LearningContinuous ControlToken-to-Token Alignment of Text Embeddings for Semantic Blending
In modern generative models, images are specified and controlled through text prompts. In practice, images are generated from sequences of tokens derived from these prompts. However, the space of token sequences lacks a …
Semantic correspondenceSemantic SimilarityContinuous ControlLow-power analogue neural networks with trainable nonlinear connections for continuous control
Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as scalar weights. Inspired by Kolmogorov-Arn…
Continuous ControlPoint TrackingObjective-Behavior Alignment: Diagnostics for MORL Policy Selection
Real-world decision-making often requires optimizing multiple competing objectives simultaneously. In reinforcement learning (RL), this is typically addressed by combining reward signals into a single scalar objective vi…
Reinforcement LearningContinuous ControlBackpropagating Through Simulation: Analytic Policy Gradients for Sample and Learning Efficient Differentiable Continuous Control
Model-free reinforcement learning algorithms such as Proximal Policy Optimization (PPO) treat the environment as a black box, estimating policy gradients from sampled rewards; this process demands millions of interaction…
Reinforcement LearningContinuous ControlADaPT: Token-Level Decoupling for Efficient Large Reasoning Models
Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-oriented methods attempt to shorten or mix rea…
Continuous ControlFinetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during do…
Continuous ControlProvably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity
NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for control tasks. However, standard offline optimisation searches for connec…
Reinforcement LearningContinuous ControlEvolutionary Bilevel Reward Shaping for Generalization in Reinforcement Learning
Reinforcement learning (RL) often suffers from performance degradation when deployed in environments that differ from those encountered during training. Existing techniques such as domain randomization (DR) mitigate this…
Reinforcement LearningBilevel OptimizationContinuous ControlBRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models
Model-based Reinforcement Learning (MBRL) has achieved remarkable success in continuous control by leveraging latent world models. However, prevailing approaches typically rely on monolithic latent dynamics, entangling e…
Reinforcement LearningContinuous ControlARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
World models are widely used in robotic and agentic engineering control systems due to their ability to learn latent dynamics for planning and decision-making. As these systems are increasingly deployed in safety-critica…
Adversarial RobustnessContinuous ControlGaze Heads: How VLMs Look at What They Describe
How a vision-language model internally solves the task of describing an image is far from obvious. We find that the model develops a specific mechanism for this: a small set of attention heads in its language-model backb…
Continuous ControlLabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan p…
Continuous ControlScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering
Aerial Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (UAVs) to actively perceive the environment and answer natural language questions. Existing outdoor EQA systems usually stop once the target ente…
Multimodal ReasoningContinuous ControlQuestion AnsweringTest-Time Gradient Guidance of Flow Policies in Reinforcement Learning
Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in …
Reinforcement LearningContinuous ControlOffline RLDifference-Aware Retrieval Policies for Imitation Learning
Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusing the training data during inference vi…
Continuous ControlPRISM: PRior-guided Imagination Sampling in world Models
A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends critically on how candidate actions are generated for model-based planni…
Continuous ControlPhysical Intuition