These 22 RSS 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 RSS 2023 paper, free trial on arXivSub.
Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments
Zirui Xu (University of Michigan), Vasileios Tzoumas (University of Michigan)
🎯 What it does: This paper proposes the Bandit Sequential Greedy algorithm for submodular optimization in multi-robot collaboration within unpredictable and partially observable environments, leveraging bandit feedback.
CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data
Sheng Zhong (University of Michigan), Nima Fazeli (University of Michigan)
CodePose EstimationOptimizationPoint Cloud
🎯 What it does: Proposes a feasible pose estimation framework named CHSEL, which integrates tactile, free space, and target volume information to generate diverse feasible pose sets from sparse contact data.
🎯 What it does: Propose a fully decentralized and accelerated bundle adjustment method called DABA, achieving scalable optimization for large-scale 3D reconstruction;
Demonstrating RFUniverse: A Multiphysics Simulation Platform for Embodied AI
Haoyuan Fu (Shanghai Jiao Tong University), Cewu Lu (Shanghai Jiao Tong University)
CodeRobotic IntelligenceTransformerReinforcement LearningMultimodalityPhysics Related
🎯 What it does: Built RFUniverse — a Unity-based multi-physics coupled simulation platform supporting rigid bodies, multi-body dynamics, gas-liquid interactions, and heat transfer, providing complete interaction and training functions through gRPC, Python, VR, ROS-free MoveIt, and gym-style interfaces; conducted reinforcement learning tasks such as food cutting, water pushing, and towel capturing, as well as planning and control experiments for butter pushing on this platform.
🎯 What it does: Utilizing deep reinforcement learning and large-scale GPU parallel physics simulation (Isaac Gym), the Allegro Hand + Kuka arm system is trained to perform complex tasks such as grasping, throwing, and repositioning. A distributed, decentralized Population Based Training (PBT) significantly enhances exploration efficiency and hyperparameter search.
Efficient volumetric mapping of multi-scale environments using wavelet-based compression
Victor Reijgwart (ETH Zurich), Lionel Ott (ETH Zurich)
CodeCompressionComputational EfficiencySimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposes a hierarchical voxel map framework based on wavelet compression (wavemap), achieving efficient voxel mapping and real-time updates for multi-scale environments.
Fast Monocular Visual-Inertial Initialization Leveraging Learned Single-View Depth
Nathaniel W Merrill (University of Delaware), Guoquan Huang (University of Delaware)
CodePose EstimationDepth EstimationConvolutional Neural NetworkSimultaneous Localization and MappingImage
🎯 What it does: Propose a linear initialization method utilizing a single-image scale-free depth network, capable of rapidly and robustly recovering the initial state of a monocular visual inertial navigation system (VINS) within an extremely short time.
Follow my Advice: Assume-Guarantee Approach to Task Planning with Human in the Loop
Georg Schuppe (KTH Royal Institute of Technology), Jana Tumova (KTH Royal Institute of Technology)
CodeRobotic Intelligence
🎯 What it does: For human-in-the-loop robot task planning, the authors adopt the assume-guarantee approach, generating minimally restrictive recommendations for humans by solving weakest sufficient assumptions, thereby achieving task execution while satisfying finite LTL constraints.
HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO
Giovanni Cioffi (University of Zurich), Davide Scaramuzza (University of Zurich)
CodePose EstimationAutonomous DrivingConvolutional Neural NetworkSimultaneous Localization and MappingMultimodalityTime SeriesPhysics Related
🎯 What it does: This paper proposes HDVIO, a method that uses a hybrid dynamic model (point mass model + learning residual) in visual-inertial odometry (VIO) to simultaneously estimate drone attitude and external forces.
InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning
Lintong Zhang (University of Oxford), Maurice Fallon (University of Oxford)
CodeSegmentationData SynthesisPose EstimationConvolutional Neural NetworkContrastive LearningSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Developed an indoor global localization system (InstaLoc) based on a single LiDAR scan, achieving localization against a prior map through instance-level semantic segmentation and descriptor matching.
🎯 What it does: Proposes a deep end-to-end path planning framework based on Imperative Learning, generating smooth, collision-free navigation paths through a network and differentiable trajectory optimization using single-frame depth maps.
🎯 What it does: Propose a language-driven visual representation learning framework called Voltron, aiming to learn visual representations that can capture low-level spatial details and understand high-level semantics, while building a benchmark suite across five categories of robot learning tasks;
POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments
Jingxing Qian (University of Toronto Institute for Aerospace Studies and University of Toronto Robotics Institute), Angela Schoellig (University of Toronto Institute for Aerospace Studies and University of Toronto Robotics Institute)
CodeRobotic IntelligenceSimultaneous Localization and MappingMultimodalityPoint Cloud
🎯 What it does: This paper proposes POV-SLAM, a probabilistic object-aware variational SLAM framework designed for semi-static environments, capable of simultaneously estimating robot poses and object consistency while tracking changes in semi-static objects over long time spans.
Progressive Learning for Physics-informed Neural Motion Planning
Ruiqi Ni (Purdue University), Ahmed H Qureshi
CodeRobotic IntelligenceConvolutional Neural NetworkPhysics Related
🎯 What it does: This study proposes a physics-informed neural motion planning method, utilizing progressive learning and viscosity terms to rewrite the Eikonal equation, achieving high-dimensional robot path planning without requiring expert trajectories.
Challen Enninful Adu (University of Michigan), Ram Vasudevan (University of Michigan)
CodeAutonomous DrivingOptimization
🎯 What it does: Proposes RADIUS—a real-time trajectory planning framework based on reachability and risk constraints in dynamic environments with uncertain obstacle positions.
Reconfigurable Robot Control Using Flexible Coupling Mechanisms
Sha Yi (Carnegie Mellon University), Zeynep Temel (Carnegie Mellon University)
CodeOptimizationRobotic Intelligence
🎯 What it does: This paper designs a flexible soft anchor coupling mechanism, enabling robots to easily couple and decouple without consuming additional energy, while maintaining structural stability in multi-robot configurations;
Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data
Tim Y. Tang (University of Oxford), Paul M Newman (University of Oxford)
CodeRecognitionDomain AdaptationAutonomous DrivingGenerative Adversarial NetworkContrastive LearningSimultaneous Localization and MappingImageMultimodalityPoint Cloud
🎯 What it does: Propose a self-supervised place recognition framework for LiDAR and satellite images without paired data, leveraging generated synthetic LiDAR, de-aliasing similarity matrices, and sequence alignment to automatically mine pseudo pairs for metric learning.
Task-Aware Risk Estimation of Perception Failures for Autonomous Vehicles
Pasquale Antonante (Massachusetts Institute of Technology), Marco Pavone (NVIDIA)
CodeAutonomous DrivingMultimodality
🎯 What it does: Propose a task-aware risk estimation framework to assess the risk of perception failure on autonomous vehicle motion planning and provide a decision-making algorithm for triggering safety actions.
Dayi E Dong (Yale University), Ian Abraham (Yale University)
CodeOptimizationRobotic IntelligenceTime Series
🎯 What it does: Propose a time-optimal Ergodic search method that generates coverage trajectories in the shortest time under a given information distribution, balancing search quality and time cost.
To the Noise and Back: Diffusion for Shared Autonomy
Takuma Yoneda (Toyota Technological Institute at Chicago), Matthew R Walter
CodeRobotic IntelligenceDiffusion modelSequential
🎯 What it does: This paper proposes a shared autonomy method based on diffusion models, which maps user actions to a trained target behavior distribution by utilizing partial forward and backward diffusion processes, thereby achieving human-machine collaborative control without requiring environmental dynamics, target spaces, or reward information.