pwc-archive/papers-with-abstracts huggingface dataset:
Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism
Authors: Haoyuan Cai, Zhenghao Peng, Bolei Zhou
Date: 2025-06-10 00:00:00
Tasks: Imitation Learning

Interactive Imitation Learning (IIL) allows agents to acquire desired behaviors through human interventions, but current methods impose high cognitive demands on human supervisors. We propose the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations. AIM utilizes a proxy Q-function to mimic the human intervention rule and adjusts intervention requests based on the alignment between agent and human actions. By assigning high Q-values when the agent deviates from the expert and decreasing these values as the agent becomes proficient, the proxy Q-function enables the agent to assess the real-time alignment with the expert and request assistance when needed. Our expert-in-the-loop experiments reveal that AIM significantly reduces expert monitoring efforts in both continuous and discrete control tasks. Compared to the uncertainty-based baseline Thrifty-DAgger, our method achieves a 40% improvement in terms of human take-over cost and learning efficiency. Furthermore, AIM effectively identifies safety-critical states for expert assistance, thereby collecting higher-quality expert demonstrations and reducing overall expert data and environment interactions needed. Code and demo video are available at https://github.com/metadriverse/AIM.
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Authors: Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer, Rolf Findeisen
Date: 2025-06-10 00:00:00
Tasks: Bayesian Optimization, Transfer Learning

Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a multi-fidelity Bayesian optimization approach that efficiently learns optimal controller parameters by leveraging both low-fidelity simulation data and a very limited number of real-world experiments. Our approach significantly reduces the need for manual tuning and expensive field testing while maintaining the standard two-stage development workflow used in industry. The core contribution is the integration of an auto-regressive multi-fidelity Gaussian process model into Bayesian optimization, enabling knowledge transfer between different fidelity levels without requiring additional low-fidelity evaluations during real-world testing. We validate our approach through both simulation studies and realworld experiments. The results demonstrate that our method achieves high-quality controller performance with only very few real-world experiments, highlighting its potential as a practical and scalable solution for intelligent vehicle control tuning in industrial applications.
DEKC: Data-Enable Control for Tethered Space Robot Deployment in the Presence of Uncertainty via Koopman Operator Theory
Authors: Ao Jin, Qinyi Wang, Sijie Wen, Ya Liu, Ganghui Shen, Panfeng Huang, Fan Zhang
Date: 2025-06-10 00:00:00

This work focuses the deployment of tethered space robot in the presence of unknown uncertainty. A data-enable framework called DEKC which contains offline training part and online execution part is proposed to deploy tethered space robot in the presence of uncertainty. The main idea of this work is modeling the unknown uncertainty as a dynamical system, which enables high accuracy and convergence of capturing uncertainty. The core part of proposed framework is a proxy model of uncertainty, which is derived from data-driven Koopman theory and is separated with controller design. In the offline stage, the lifting functions associated with Koopman operator are parameterized with deep neural networks. Then by solving an optimization problem, the lifting functions are learned from sampling data. In the online execution stage, the proxy model cooperates the learned lifting functions obtained in the offline phase to capture the unknown uncertainty. Then the output of proxy model is compensated to the baseline controller such that the effect of uncertainty can be attenuated or even eliminated. Furthermore, considering some scenarios in which the performance of proxy model may weaken, a receding-horizon scheme is proposed to update the proxy model online. Finally, the extensive numerical simulations demonstrate the effectiveness of our proposed framework. The implementation of proposed DEKC framework is publicly available at https://github.com/NPU-RCIR/DEKC.git.
Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards
Authors: Ali Abedi, Fernando Cladera, Mohsen Farajijalal, Reza Ehsani
Date: 2025-06-09 00:00:00
Tasks: Clustering

We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation. Unlike prior approaches that rely on static scans or assume uniform orchard structures, our method adapts to varying field geometries via an integrated pipeline of LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction. We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns. A hybrid clustering strategy combining DBSCAN and spectral clustering enables robust per-tree segmentation, achieving 93% success in pistachio and 80% in almond, with strong agreement to drone derived canopy volume estimates. This work advances scalable, non-intrusive tree monitoring for structurally diverse orchard environments.
Real-Time Execution of Action Chunking Flow Policies
Authors: Kevin Black, Manuel Y. Galliker, Sergey Levine
Date: 2025-06-09 00:00:00
Tasks: Chunking, Vision-Language-Action

Modern AI systems, especially those interacting with the physical world, increasingly require real-time performance. However, the high latency of state-of-the-art generalist models, including recent vision-language action models (VLAs), poses a significant challenge. While action chunking has enabled temporal consistency in high-frequency control tasks, it does not fully address the latency problem, leading to pauses or out-of-distribution jerky movements at chunk boundaries. This paper presents a novel inference-time algorithm that enables smooth asynchronous execution of action chunking policies. Our method, real-time chunking (RTC), is applicable to any diffusion- or flow-based VLA out of the box with no re-training. It generates the next action chunk while executing the current one, "freezing" actions guaranteed to execute and "inpainting" the rest. To test RTC, we introduce a new benchmark of 12 highly dynamic tasks in the Kinetix simulator, as well as evaluate 6 challenging real-world bimanual manipulation tasks. Results demonstrate that RTC is fast, performant, and uniquely robust to inference delay, significantly improving task throughput and enabling high success rates in precise tasks $\unicode{x2013}$ such as lighting a match $\unicode{x2013}$ even in the presence of significant latency. See https://pi.website/research/real_time_chunking for videos.
Hierarchical Scoring with 3D Gaussian Splatting for Instance Image-Goal Navigation
Authors: Yijie Deng, Shuaihang Yuan, Geeta Chandra Raju Bethala, Anthony Tzes, Yu-Shen Liu, Yi Fang
Date: 2025-06-09 00:00:00
Tasks: 3DGS, Navigate, Novel View Synthesis, Pose Estimation, Semantic Similarity, Semantic Textual Similarity

Instance Image-Goal Navigation (IIN) requires autonomous agents to identify and navigate to a target object or location depicted in a reference image captured from any viewpoint. While recent methods leverage powerful novel view synthesis (NVS) techniques, such as three-dimensional Gaussian splatting (3DGS), they typically rely on randomly sampling multiple viewpoints or trajectories to ensure comprehensive coverage of discriminative visual cues. This approach, however, creates significant redundancy through overlapping image samples and lacks principled view selection, substantially increasing both rendering and comparison overhead. In this paper, we introduce a novel IIN framework with a hierarchical scoring paradigm that estimates optimal viewpoints for target matching. Our approach integrates cross-level semantic scoring, utilizing CLIP-derived relevancy fields to identify regions with high semantic similarity to the target object class, with fine-grained local geometric scoring that performs precise pose estimation within promising regions. Extensive evaluations demonstrate that our method achieves state-of-the-art performance on simulated IIN benchmarks and real-world applicability.
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI
Authors: Aditya Chakravarty
Date: 2025-06-08 00:00:00
Tasks: Deblurring, Denoising, Image Restoration, Super-Resolution

Diffusion models have shown remarkable flexibility for solving inverse problems without task-specific retraining. However, existing approaches such as Manifold Preserving Guided Diffusion (MPGD) apply only a single gradient update per denoising step, limiting restoration fidelity and robustness, especially in embedded or out-of-distribution settings. In this work, we introduce a multistep optimization strategy within each denoising timestep, significantly enhancing image quality, perceptual accuracy, and generalization. Our experiments on super-resolution and Gaussian deblurring demonstrate that increasing the number of gradient updates per step improves LPIPS and PSNR with minimal latency overhead. Notably, we validate this approach on a Jetson Orin Nano using degraded ImageNet and a UAV dataset, showing that MPGD, originally trained on face datasets, generalizes effectively to natural and aerial scenes. Our findings highlight MPGD's potential as a lightweight, plug-and-play restoration module for real-time visual perception in embodied AI agents such as drones and mobile robots.
Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers
Authors: Hikaru Sawafuji, Ryota Ozaki, Takuto Motomura, Toyohisa Matsuda, Masanori Tojima, Kento Uchida, Shinichi Shirakawa
Date: 2025-06-08 00:00:00
Tasks: Position

Self-localization is an important technology for automating bulldozers. Conventional bulldozer self-localization systems rely on RTK-GNSS (Real Time Kinematic-Global Navigation Satellite Systems). However, RTK-GNSS signals are sometimes lost in certain mining conditions. Therefore, self-localization methods that do not depend on RTK-GNSS are required. In this paper, we propose a machine learning-based self-localization method for bulldozers. The proposed method consists of two steps: estimating local velocities using a machine learning model from internal sensors, and incorporating these estimates into an Extended Kalman Filter (EKF) for global localization. We also created a novel dataset for bulldozer odometry and conducted experiments across various driving scenarios, including slalom, excavation, and driving on slopes. The result demonstrated that the proposed self-localization method suppressed the accumulation of position errors compared to kinematics-based methods, especially when slip occurred. Furthermore, this study showed that bulldozer-specific sensors, such as blade position sensors and hydraulic pressure sensors, contributed to improving self-localization accuracy.
BG-HOP: A Bimanual Generative Hand-Object Prior
Authors: Sriram Krishna, Sravan Chittupalli, Sungjae Park
Date: 2025-06-08 00:00:00

In this work, we present BG-HOP, a generative prior that seeks to model bimanual hand-object interactions in 3D. We address the challenge of limited bimanual interaction data by extending existing single-hand generative priors, demonstrating preliminary results in capturing the joint distribution of hands and objects. Our experiments showcase the model's capability to generate bimanual interactions and synthesize grasps for given objects. We make code and models publicly available.
Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search
Authors: Dongryung Lee, Sejune Joo, Kimin Lee, Beomjoon Kim
Date: 2025-06-08 00:00:00
Tasks: Common Sense Reasoning, Motion Planning, Task and Motion Planning, Task Planning

The problem of relocating a set of objects to designated areas amidst movable obstacles can be framed as a Geometric Task and Motion Planning (G-TAMP) problem, a subclass of task and motion planning (TAMP). Traditional approaches to G-TAMP have relied either on domain-independent heuristics or on learning from planning experience to guide the search, both of which typically demand significant computational resources or data. In contrast, humans often use common sense to intuitively decide which objects to manipulate in G-TAMP problems. Inspired by this, we propose leveraging Large Language Models (LLMs), which have common sense knowledge acquired from internet-scale data, to guide task planning in G-TAMP problems. To enable LLMs to perform geometric reasoning, we design a predicate-based prompt that encodes geometric information derived from a motion planning algorithm. We then query the LLM to generate a task plan, which is then used to search for a feasible set of continuous parameters. Since LLMs are prone to mistakes, instead of committing to LLM's outputs, we extend Monte Carlo Tree Search (MCTS) to a hybrid action space and use the LLM to guide the search. Unlike the previous approach that calls an LLM at every node and incurs high computational costs, we use it to warm-start the MCTS with the nodes explored in completing the LLM's task plan. On six different G-TAMP problems, we show our method outperforms previous LLM planners and pure search algorithms. Code can be found at: https://github.com/iMSquared/prime-the-search
CARoL: Context-aware Adaptation for Robot Learning
Authors: Zechen Hu, Tong Xu, Xuesu Xiao, Xuan Wang
Date: 2025-06-08 00:00:00
Tasks: Reinforcement Learning (RL)

Using Reinforcement Learning (RL) to learn new robotic tasks from scratch is often inefficient. Leveraging prior knowledge has the potential to significantly enhance learning efficiency, which, however, raises two critical challenges: how to determine the relevancy of existing knowledge and how to adaptively integrate them into learning a new task. In this paper, we propose Context-aware Adaptation for Robot Learning (CARoL), a novel framework to efficiently learn a similar but distinct new task from prior knowledge. CARoL incorporates context awareness by analyzing state transitions in system dynamics to identify similarities between the new task and prior knowledge. It then utilizes these identified similarities to prioritize and adapt specific knowledge pieces for the new task. Additionally, CARoL has a broad applicability spanning policy-based, value-based, and actor-critic RL algorithms. We validate the efficiency and generalizability of CARoL on both simulated robotic platforms and physical ground vehicles. The simulations include CarRacing and LunarLander environments, where CARoL demonstrates faster convergence and higher rewards when learning policies for new tasks. In real-world experiments, we show that CARoL enables a ground vehicle to quickly and efficiently adapt policies learned in simulation to smoothly traverse real-world off-road terrain.
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
Authors: Clinton Enwerem, Aniruddh G. Puranic, John S. Baras, Calin Belta
Date: 2025-06-08 00:00:00
Tasks: quantile regression, Reinforcement Learning (RL)

Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic environments. Quantile-based action-value iteration methods reduce this bias by learning a distribution of the expected cost-to-go using quantile regression. However, ensuring that the learned policy satisfies safety constraints remains a challenge when these constraints are not explicitly integrated into the RL framework. Existing methods often require complex neural architectures or manual tradeoffs due to combined cost functions. To address this, we propose a risk-regularized quantile-based algorithm integrating Conditional Value-at-Risk (CVaR) to enforce safety without complex architectures. We also provide theoretical guarantees on the contraction properties of the risk-sensitive distributional Bellman operator in Wasserstein space, ensuring convergence to a unique cost distribution. Simulations of a mobile robot in a dynamic reach-avoid task show that our approach leads to more goal successes, fewer collisions, and better safety-performance trade-offs compared to risk-neutral methods.
Towards Data-Driven Model-Free Safety-Critical Control
Authors: Zhe Shen, Yitaek Kim, Christoffer Sloth
Date: 2025-06-07 00:00:00

This paper presents a framework for enabling safe velocity control of general robotic systems using data-driven model-free Control Barrier Functions (CBFs). Model-free CBFs rely on an exponentially stable velocity controller and a design parameter (e.g. alpha in CBFs); this design parameter depends on the exponential decay rate of the controller. However, in practice, the decay rate is often unavailable, making it non-trivial to use model-free CBFs, as it requires manual tuning for alpha. To address this, a Neural Network is used to learn the Lyapunov function from data, and the maximum decay rate of the systems built-in velocity controller is subsequently estimated. Furthermore, to integrate the estimated decay rate with model-free CBFs, we derive a probabilistic safety condition that incorporates a confidence bound on the violation rate of the exponential stability condition, using Chernoff bound. This enhances robustness against uncertainties in stability violations. The proposed framework has been tested on a UR5e robot in multiple experimental settings, and its effectiveness in ensuring safe velocity control with model-free CBFs has been demonstrated.
Multimodal Spatial Language Maps for Robot Navigation and Manipulation
Authors: Chenguang Huang, Oier Mees, Andy Zeng, Wolfram Burgard
Date: 2025-06-07 00:00:00
Tasks: 3D Reconstruction, Robot Navigation

Grounding language to a navigating agent's observations can leverage pretrained multimodal foundation models to match perceptions to object or event descriptions. However, previous approaches remain disconnected from environment mapping, lack the spatial precision of geometric maps, or neglect additional modality information beyond vision. To address this, we propose multimodal spatial language maps as a spatial map representation that fuses pretrained multimodal features with a 3D reconstruction of the environment. We build these maps autonomously using standard exploration. We present two instances of our maps, which are visual-language maps (VLMaps) and their extension to audio-visual-language maps (AVLMaps) obtained by adding audio information. When combined with large language models (LLMs), VLMaps can (i) translate natural language commands into open-vocabulary spatial goals (e.g., "in between the sofa and TV") directly localized in the map, and (ii) be shared across different robot embodiments to generate tailored obstacle maps on demand. Building upon the capabilities above, AVLMaps extend VLMaps by introducing a unified 3D spatial representation integrating audio, visual, and language cues through the fusion of features from pretrained multimodal foundation models. This enables robots to ground multimodal goal queries (e.g., text, images, or audio snippets) to spatial locations for navigation. Additionally, the incorporation of diverse sensory inputs significantly enhances goal disambiguation in ambiguous environments. Experiments in simulation and real-world settings demonstrate that our multimodal spatial language maps enable zero-shot spatial and multimodal goal navigation and improve recall by 50% in ambiguous scenarios. These capabilities extend to mobile robots and tabletop manipulators, supporting navigation and interaction guided by visual, audio, and spatial cues.
RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks
Authors: Shiying Duan, Pei Ren, Nanxiang Jiang, Zhengping Che, Jian Tang, Yifan Sun, Zhaoxin Fan, Wenjun Wu
Date: 2025-06-07 00:00:00
Tasks: Large Language Model, Task Planning

Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios. While existing methods have achieved promising results in task planning, they often fail to fully optimize task parallelism, limiting the potential of dual-arm collaboration. To address this issue, we propose RoboPARA, a novel large language model (LLM)-driven framework for dual-arm task parallelism planning. RoboPARA employs a two-stage process: (1) Dependency Graph-based Planning Candidates Generation, which constructs directed acyclic graphs (DAGs) to model task dependencies and eliminate redundancy, and (2) Graph Re-Traversal-based Dual-Arm Parallel Planning, which optimizes DAG traversal to maximize parallelism while maintaining task coherence. In addition, we introduce the Cross-Scenario Dual-Arm Parallel Task dataset (X-DAPT dataset), the first dataset specifically designed to evaluate dual-arm task parallelism across diverse scenarios and difficulty levels. Extensive experiments on the X-DAPT dataset demonstrate that RoboPARA significantly outperforms existing methods, achieving higher efficiency and reliability, particularly in complex task combinations. The code and dataset will be released upon acceptance.
RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation
Authors: Songhao Han, Boxiang Qiu, Yue Liao, Siyuan Huang, Chen Gao, Shuicheng Yan, Si Liu
Date: 2025-06-07 00:00:00
Tasks: Vision-Language-Action

Recent advances in vision-language models (VLMs) have enabled instruction-conditioned robotic systems with improved generalization. However, most existing work focuses on reactive System 1 policies, underutilizing VLMs' strengths in semantic reasoning and long-horizon planning. These System 2 capabilities-characterized by deliberative, goal-directed thinking-remain under explored due to the limited temporal scale and structural complexity of current benchmarks. To address this gap, we introduce RoboCerebra, a benchmark for evaluating high-level reasoning in long-horizon robotic manipulation. RoboCerebra includes: (1) a large-scale simulation dataset with extended task horizons and diverse subtask sequences in household environments; (2) a hierarchical framework combining a high-level VLM planner with a low-level vision-language-action (VLA) controller; and (3) an evaluation protocol targeting planning, reflection, and memory through structured System 1-System 2 interaction. The dataset is constructed via a top-down pipeline, where GPT generates task instructions and decomposes them into subtask sequences. Human operators execute the subtasks in simulation, yielding high-quality trajectories with dynamic object variations. Compared to prior benchmarks, RoboCerebra features significantly longer action sequences and denser annotations. We further benchmark state-of-the-art VLMs as System 2 modules and analyze their performance across key cognitive dimensions, advancing the development of more capable and generalizable robotic planners.