The world’s top robotics minds are gathering at ICRA 2025 in Atlanta, Georgia this month, and we’re proud to see Clearpath robots helping drive the research conversation. From novel applications in autonomous navigation to advancements in human-robot interaction, researchers across institutions are using our platforms to explore a range of challenges and solutions in the field. In this blog post, we highlight five papers offering a closer look at how our robots are being used to propel robotics research.
QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning
Clearpath Platform: Jackal
Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement learning methods often assume symmetric costs, which can lead to suboptimal navigation paths and increased safety risks in real-world scenarios. In this paper, we introduce QuasiNav, a novel reinforcement learning framework that integrates quasimetric embeddings to explicitly model asymmetric costs and guide efficient, safe navigation. QuasiNav formulates the navigation problem as a constrained Markov decision process (CMDP) and employs quasimetric embeddings to capture directionally dependent costs, allowing for a more accurate representation of the terrain. We combine this approach with adaptive constraint tightening. This ensures that safety constraints are dynamically enforced during learning. We validate QuasiNav on a Clearpath Jackal robot in three challenging navigation scenarios—undulating terrains, asymmetric hill traversal, and directionally dependent terrain traversal—demonstrating its effectiveness in both simulated and real-world environments. Experimental results show that QuasiNav significantly outperforms conventional methods, achieving higher success rates, improved energy efficiency (13.6% reduction in energy consumption compared to baseline methods), and better adherence to safety constraints.
Hossain, Jumman – University of Maryland, Baltimore County
Faridee, Abu-Zaher – University of Maryland, Baltimore County
Asher, Derrik – DEVCOM Army Research Lab
Freeman, Jade – DEVCOM Army Research Lab
Gregory, Timothy – DEVCOM Army Research Lab
Trout, Theron T. – Stormfish Scientific Corp
Roy, Nirmalya – University of Maryland, Baltimore County
VLM-GroNav: Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments
Clearpath Platform: Husky
We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical grounding that is used to assess intrinsic terrain properties such as deformability and slipperiness. We use proprioceptive-based sensing, which provides direct measurements of these physical properties, and enhances the overall semantic understanding of the terrains. Our formulation uses in-context learning to ground the VLM’s semantic understanding with proprioceptive data to allow dynamic updates of traversability estimates based on the robot’s real-time physical interactions with the environment. We use the updated traversability estimations to inform both the local and global planners for real-time trajectory replanning. We validate our method on a legged robot (Ghost Vision 60) and a wheeled robot (Clearpath Husky), in diverse real-world outdoor environments with different deformable and slippery terrains. In practice, we observe significant improvements over state-of-the-art methods by up to 50% increase in navigation success rate.
Elnoor, Mohamed – University of Maryland
Kulathun Mudiyanselage, Kasun Weerakoon – University of Maryland, College Park
Seneviratne, Gershom Devake – University of Maryland, College Park
Xian, Ruiqi – University of Maryland, College Park
Guan, Tianrui – University of Maryland
M Jaffar, Mohamed Khalid – University of Maryland, College Park
Rajagopal, Vignesh – University of Maryland, College Park
Manocha, Dinesh – University of Maryland
“Don’t Forget to Put the Milk Back!” Dataset for Enabling Embodied Agents to Detect Anomalous Situations
Clearpath Platform: TurtleBot
Home robots intend to make their users’ lives easier. Our work aims to assist in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or leaving poison accessible to children. To move towards enabling home robots with these abilities, we have created a new dataset, which we call SafetyDetect. The SafetyDetect dataset consists of 1000 anomalous home scenes, each of which contains unsafe or unsanitary situations for an agent to detect. Our approach utilizes large language models (LLMs) alongside both a graph representation of the scene and the relationships between the objects in the scene. Our key insight is that this connected scene graph and the object relationships it encodes enables the LLM to better reason about the scene — especially as it relates to detecting dangerous or unsanitary situations. Our most promising approach utilizes GPT-4 and pursues a classification technique where object relations from the scene graph are classified as normal, dangerous, unsanitary, or dangerous for children. This method is able to correctly identify over 90% of anomalous scenarios in the SafetyDetect Dataset. Additionally, we conduct real world experiments on a Clearpath TurtleBot where we generate a scene graph from visuals of the real world scene, and run our approach with no modification. This setup resulted in little performance loss. The SafetyDetect dataset and code will be released to the public upon this paper’s publication.
Mullen, James – University of Maryland
Goyal, Prasoon – Amazon
Piramuthu, Robinson – Amazon
Johnston, Michael – Amazon
Manocha, Dinesh – University of Maryland
Ghanadan, Reza – Amazon
Jailbreaking LLM-Controlled Robots
Clearpath Platform: Jackal
The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When viewed as a stand-alone technology, LLMs are known to be vulnerable to jailbreaking attacks, wherein malicious prompters elicit harmful text by bypassing LLM safety guardrails. To assess the risks of deploying LLMs in robotics, in this paper, we introduce RoboPAIR, the first algorithm designed to jailbreak LLM-controlled robots. Unlike existing, textual attacks on LLM chatbots, RoboPAIR elicits harmful physical actions from LLM-controlled robots, a phenomenon we experimentally demonstrate in three scenarios: (i) a white-box setting, wherein the attacker has full access to the NVIDIA Dolphins self-driving LLM, (ii) a gray-box setting, wherein the attacker has partial access to a Clearpath Robotics Jackal UGV robot equipped with a GPT-4o planner, and (iii) a black-box setting, wherein the attacker has only query access to the GPT-3.5-integrated Unitree Robotics Go2 robot dog. In each scenario and across three new datasets of harmful robotic actions, we demonstrate that RoboPAIR, as well as several static baselines, finds jailbreaks quickly and effectively, often achieving 100% attack success rates. Our results reveal, for the first time, that the risks of jailbroken LLMs extend far beyond text generation, given the distinct possibility that jailbroken robots could cause physical damage in the real world. Indeed, our results on the Unitree Go2 represent the first successful jailbreak of a deployed commercial robotic system. Addressing this emerging vulnerability is critical for ensuring the safe deployment of LLMs in robotics. Additional media is available at: https://robopair.org.
Robey, Alexander – University of Pennsylvania
Ravichandran, Zachary – University of Pennsylvania
Kumar, Vijay – University of Penn
Hassani, Hamed – University of Pennsylvania
Pappas, George J. – University of Pennsylvania
Risk-Aware Energy-Constrained UAV-UGV Cooperative Routing Using Attention-Guided Reinforcement Learning
Clearpath Platform: Husky
Maximizing the endurance of unmanned aerial vehicles (UAVs) in large-scale monitoring missions spanning over large areas requires addressing their limited battery capacity. Deploying unmanned ground vehicles (UGVs) as mobile recharging stations offers a practical solution, extending UAVs’ operational range. This introduces the challenge of optimizing UAV-UGV routes for efficient mission point coverage and seamless recharging coordination. In this paper, we present a risk-aware deep reinforcement learning (Ra-DRL) framework with a multi-head attention mechanism within an encoder-decoder transformer architecture to solve this cooperative routing problem for a UAV-UGV team. Our model minimizes mission time while accounting for the stochastic fuel consumption of the UAV, influenced by environmental factors like wind velocity, ensuring adherence to a risk threshold to avoid mid-mission energy depletion. Extensive evaluations on various problem sizes show that our method significantly outperforms nearest-neighbor heuristics in both solution quality and risk management. We validate the Ra-DRL policy in a Gazebo-ROS SITL environment with a PX4-based custom UAV and Clearpath Husky UGV. The results demonstrate the robustness and adaptability of our policy, making it highly effective for mission planning in dynamic, uncertain scenarios.
Mondal, Mohammad Safwan – University of Illinois Chicago
Ramasamy, Subramanian – University of Illinois Chicago
Rownak, Ragib – University of Illinois Chicago
Russo, Luca – University of Illinois Chicago
Humann, James – DEVCOM Army Research Laboratory
James, Dotterweich, Jim – Army Research Laboratory
Bhounsule, Pranav – University of Illinois at Chicago
We applaud the impressive research done by these five groups using Clearpath Robotics platforms. It’s great to see the range of applications and ideas coming out of the academic community. We look forward to seeing you at ICRA in a couple weeks!
Check out all the ICRA Technical Program paper submissions here.