These 18 CoRL 2025 papers come with a code repository. Each shows an AI one-line summary below — get the verified repo link + the full 6-part summary (innovation, method, data, results, limitations) and search every CoRL 2025 paper, free trial on arXivSub.
$Door(s)$: Junction State Estimation for Efficient Exploration in Reinforcement Learning
Benjamin Fele (Jožef Stefan Institute), Jan Babic (Jožef Stefan Institute)
CodeReinforcement Learning
🎯 What it does: Proposes a heuristic method for door state estimation (Doorfs) to promote efficient exploration in reinforcement learning by identifying narrow passages or joints in the state space.
🎯 What it does: Studied the scalability of using reinforcement learning (RL) for planning in autonomous driving, proposing the CaRL method that uses only a route completion reward, and trained and evaluated on CARLA and nuPlan.
ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusion
Zichao Hu (University of Texas at Austin), Joydeep Biswas (University of Texas at Austin)
CodeRobotic IntelligenceReinforcement LearningDiffusion model
🎯 What it does: Studied generating robot navigation trajectories that meet multiple specifications based on natural language instructions in dynamic environments.
From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Naman Shah (Arizona State University), Siddharth Srivastava (Arizona State University)
CodeRobotic IntelligenceWorld ModelSequential
🎯 What it does: This paper proposes the LAMP (Learning Abstract Models for Planning) method, which can automatically invent symbolic relational concepts and world models from a small number of unlabeled, unsegmented motion trajectories, enabling robots to achieve zero-shot reasoning and planning in unseen, more complex long-horizon tasks.
LaDi-WM: A Latent Diffusion-Based World Model for Predictive Manipulation
Yuhang Huang, Kai Xu (National University Of Defense Technology)
CodeRobotic IntelligenceTransformerVision Language ModelDiffusion modelWorld ModelImageVideo
🎯 What it does: Propose a latent diffusion world model LaDi-WM that leverages visual foundation models to predict future latent states of robot-object interactions and perform action planning based on these predictions.
🎯 What it does: Propose the SPARTA method, which leverages dense point clouds to learn smooth traversability estimates for vehicles at different entry angles, outputting corresponding risk distributions; during planning, risk awareness based on angles is achieved by querying this distribution.
🎯 What it does: Developed a multimodal fusion learning framework, MMFL, for solving the generalized traveling salesman problem in mobile robot task planning, combining graph structures with spatial image representations to achieve high-quality real-time path planning.
🎯 What it does: Developed the Omni-Perception framework, achieving omnidirectional collision avoidance and efficient motion control for legged robots through end-to-end reinforcement learning using raw LiDAR point clouds.
🎯 What it does: Proposes a scene recognition framework named OPAL for mapping single-frame LiDAR data to OpenStreetMap (OSM), achieving meter-level localization with only a single LiDAR scan.
🎯 What it does: Proposes the PicoPose framework, achieving zero-shot new object pose estimation using only RGB images through three-stage pixel-level correspondence learning.
🎯 What it does: Proposed a novel autonomous driving evaluation paradigm called Pseudo-Simulation, combining real-world data with pre-rendered synthetic observations to assess vehicle behavior in a more efficient and reproducible manner.
🎯 What it does: Proposed a controllable panoramic video generation framework called QuaDreamer for quadruped robots, which can synthesize panoramic videos with realistic vibrations and precise object movements based on single-frame images, object trajectories, and robot vibration signals.
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
Xueyi Liu (Chinese Academy of Sciences), Chen Huiyong
CodeAutonomous DrivingTransformerLarge Language ModelVision-Language-Action ModelMultimodalityChain-of-Thought
🎯 What it does: Propose ReasonPlan, a closed-loop driving framework based on multi-modal large language models (LLMs), which integrates visual information and enables interpretable decision-making through self-supervised next scene prediction (NSP) and supervised decision chain-of-thought (DeCoT); simultaneously constructs a planning-oriented decision reasoning dataset PDR with 210k samples.
🎯 What it does: Proposed a recursive Koopman learning (RKL) pipeline to achieve real-time recursive model updates and sample-efficient control strategy learning.
Sampling-based System Identification with Active Exploration for Legged Sim2Real Learning
Nikhil Sobanbabu (Carnegie Mellon University), Guanya Shi (Carnegie Mellon University)
CodeDomain AdaptationOptimizationRobotic IntelligenceReinforcement LearningTime Series
🎯 What it does: Proposes a two-stage active sampling system identification framework (SPI-Active), which estimates the physical parameters of legged robots through parallel sampling and combines active exploration to maximize Fisher information, thereby improving the accuracy of simulation-to-reality transfer.
🎯 What it does: Leverage a learning model to imitate an optimization-based time-optimal trajectory planner, rapidly generating trajectories executable in real-time on quadrotors.
CodeOptimizationRobotic IntelligenceLarge Language ModelVision Language Model
🎯 What it does: Infer task-related 6-degree-of-freedom reference frames through trajectory optimization under the condition of a single demonstration, and utilize this framework to achieve cross-scenario generalization of DMP.