These 34 CoRL 2023 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 2023 paper, free trial on arXivSub.
A Bayesian Approach to Robust Inverse Reinforcement Learning
Ran Wei (Texas Aandm University), Mingyi Hong (University Of Minnesota)
CodeReinforcement LearningBenchmark
🎯 What it does: Propose a Bayesian framework for offline model-based inverse reinforcement learning, jointly estimating the expert's reward function and its internal environment dynamics model
🎯 What it does: Proposes BM2CP, a multimodal collaborative perception framework that integrates LiDAR and camera data to achieve efficient collaborative perception.
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Jinxin Liu (Zhejiang University), Donglin Wang (Westlake University)
CodeReinforcement LearningAuto EncoderBenchmark
🎯 What it does: Proposes a framework called CLUE for offline reinforcement learning that leverages limited expert data to relabel intrinsic rewards for unlabeled transfer.
🎯 What it does: Proposed a context-aware deep reinforcement learning-based mapless mobile robot navigation framework that achieves rapid decision-making through constructing an environmental belief map.
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
Kevin Huang (University of Washington), Byron Boots (University of Washington)
CodeRobotic IntelligenceReinforcement Learning
🎯 What it does: Propose DATT, which combines reinforcement learning (RL) with L1 adaptive control to achieve trackable arbitrary trajectories under unknown disturbances.
🎯 What it does: This paper proposes the DORT framework, which leverages temporal information from multi-camera videos to jointly estimate the motion and position of dynamic objects, achieving 3D detection and tracking.
🎯 What it does: Proposes a multi-agent motion prediction and control framework based on Energy-based Potential Game (EPO), integrating neural networks for inferring game parameters with a differentiable game optimization layer;
🎯 What it does: Proposes a motion primitive model (MMP) capable of learning continuous motion trajectory manifolds, and further constructs equivariant motion primitives (EMMP) by leveraging task symmetry
🎯 What it does: Designed and implemented a learning-based tracking and cross-identification framework called HANDLOOM for spatial state estimation of long flexible linear objects (e.g., cables, ropes) in semi-planar configurations, applied to multi-cable inspection, instructional knot-tying, and robotic untangling tasks.
🎯 What it does: Investigated the combination of heteroscedastic Gaussian processes (HGP) with random features to achieve scalable motion primitives and provide theoretical guarantees.
Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model
Matan Sudry (Technion - Israel Institute of Technology), Erez Karpas (Technion - Israel Institute of Technology)
CodeRobotic IntelligenceSequential
🎯 What it does: Proposed the TWISTED system, which employs knot theory for high-level topological planning and uses a self-supervised inverse model at the low level to achieve rope knotting actions;
🎯 What it does: Proposes an open-vocabulary mobile manipulation (OVMM) benchmark tailored for home scenarios, achieving reproducible HomeRobot library and baseline algorithms in both simulated and real environments.
🎯 What it does: Construct physics-constrained dynamical models with uncertainty assessment using Neural SDEs, and validate their prediction and control performance on multiple robotic systems (spring-mass-damper, inverted pendulum, hexarotor).
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
Gaurav Datta (UC Berkeley), Ken Goldberg (UC Berkeley)
CodeRobotic IntelligenceReinforcement Learning from Human FeedbackReinforcement LearningBenchmark
🎯 What it does: Propose an Implicit Interactive Fleet Learning (IIFL) framework leveraging energy-based models, capable of learning from diverse, heterogeneous human supervisors and addressing distribution shift and multimodal problems in multi-robot environments.
Language-Guided Traffic Simulation via Scene-Level Diffusion
Ziyuan Zhong (Columbia University), Baishakhi Ray (Columbia University)
CodeGenerationAutonomous DrivingTransformerLarge Language ModelDiffusion model
🎯 What it does: Propose a language-guided scene-level conditional diffusion model for generating realistic and controllable traffic simulation trajectories.
🎯 What it does: Studied a framework that uses a standard two-finger gripper to perform mechanical search and eventually grasp completely occluded target objects on a cluttered shelf through pushing and placing actions.
OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data
Shiyang Lu, Kostas Bekris (Rutgers University)
CodeRetrievalConvolutional Neural NetworkTransformerVision Language ModelContrastive LearningTextPoint Cloud
🎯 What it does: Proposed an open-vocabulary 3D instance retrieval method called OVIR-3D that does not require 3D training. It uses a 2D open-vocabulary detector to generate text-aligned region candidates, projects and aggregates them into 3D point clouds, achieving instance-level retrieval for text queries;
🎯 What it does: Propose PairwiseNet, which estimates the global collision distance of a robotic system by learning the minimal collision distance between two geometric shapes, and then obtains the global distance by taking the minimum of all pairwise distances.
Parting with Misconceptions about Learning-based Vehicle Motion Planning
Daniel Dauner (University of Tübingen), Kashyap Chitta (University of Tübingen)
CodeAutonomous DrivingOptimization
🎯 What it does: Investigated misconceptions in learning-based vehicle motion planning and proposed a hybrid model, PDM-Hybrid, combining rule-based planning with learning-based prediction.
🎯 What it does: Propose a brain-body pre-training based co-design method called PreCo, which achieves multi-task zero-shot generalization and few-shot fine-tuning on modular soft robots through a generic co-design strategy.
🎯 What it does: Developed a sequential latent temporal model called SLaTe-PRO for predicting daily object usage in robot-assisted environments, supporting proactive assistance and interactive queries.
CodeReinforcement Learning from Human FeedbackReinforcement LearningContrastive LearningTabularSequentialBenchmark
🎯 What it does: Integrate self-supervised temporal consistency tasks with preference learning to construct a reward function that encodes dynamics, significantly improving sample efficiency in preference-based reinforcement learning.
CodeRobotic IntelligenceRecurrent Neural NetworkTransformerLarge Language ModelVision-Language-Action ModelPoint Cloud
🎯 What it does: Propose Spatial-Language Attention Policies (SLAP), a multi-task robotic control framework that integrates point clouds, language, and attention mechanisms, enabling the learning of continuous action policies from a few demonstrations in mobile manipulator and desktop scenarios.
🎯 What it does: A STAR algorithm for covert multi-agent active search under known terrain maps is studied, balancing information gain and covertness, supporting distributed decision-making under communication failure and noisy observations.
🎯 What it does: Propose an unsupervised and self-supervised terrain representation learning framework called STERLING, which learns terrain features required for visual navigation using unlabeled multimodal data collected by robots under non-expert operation, and applies these features in offline path planning tasks with preference alignment.
🎯 What it does: This paper proposes two stochastic occupancy grid map prediction methods based on variational autoencoders, SOGMP and SOGMP++, achieving probabilistic prediction of future maps for mobile robots in dynamic environments.
🎯 What it does: Utilizing a deep neural network as a proxy model to predict human-robot interaction outcomes, combined with a differentiable quality diversity (DQD) algorithm to automatically generate diverse and challenging interaction scenarios;
Towards General Single-Utensil Food Acquisition with Human-Informed Actions
Ethan Kroll Gordon, Siddhartha Srinivasa
CodeRobotic IntelligenceReinforcement Learning from Human FeedbackReinforcement LearningImageSequential
🎯 What it does: This paper collects trajectory data of humans acquiring food using a single utensil (e.g., a fork) and constructs a 26-dimensional action space, which is clustered into 11 discrete, executable action sets for robot-assisted feeding.
🎯 What it does: Proposed a virtual traffic coordinator network called TraCo for multi-agent reinforcement learning to learn and issue team collaboration instructions, enabling each vehicle to follow collective demands in traffic scenarios.
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
Daniel Widmer (ETH Zürich), Stelian Coros (ETH Zürich)
CodeOptimizationRobotic Intelligence
🎯 What it does: Automatically adjust the feedback gains of a quadruped robot's controller through safe Bayesian optimization (GOSAFEOPT), addressing the discrepancy between the model and real hardware, and achieving safe and efficient parameter optimization under different gaits.
CodeDomain AdaptationRobotic IntelligenceGraph Neural NetworkReinforcement LearningTabularTime Series
🎯 What it does: Proposes a framework called COMPASS that automatically adjusts simulation environment parameters through differentiable causal discovery methods, thereby narrowing the gap between simulation and the real world, and achieving higher trajectory alignment and task success rates in dynamic multi-object interaction tasks (e.g., mini aerial hockey).