Vijay Kumar

Micro aerial vehicles are emerging as powerful platforms for embodied autonomy in complex, GPS-denied, and hard-to-access environments. Their small size and agility allow them to operate under forest canopy, between tree lines, inside buildings, and in other cluttered settings where larger systems cannot go. At the same time, their severe constraints in sensing, computation, communication, energy, and payload make them a demanding testbed for physical AI: intelligence must be tightly coupled to perception, planning, control, and the dynamics of flight. In this talk, I will describe our work on intelligent micro aerial vehicles for autonomous operation in complex field environments. I will discuss the abstractions, architectures, and algorithms that enable robots to estimate state, reason about free space, plan dynamically feasible trajectories, and build semantic-metric maps in GPS-denied environments. I will also describe recent advances in learning-guided active perception, semantic mapping, air-ground collaboration, and language-specified missions, showing how MAVs can move beyond coverage and mapping toward task-driven information gathering. The talk will highlight applications in precision agriculture, forestry, infrastructure inspection, and search in unknown environments, with examples of autonomous flight under forest canopy, between tree rows, and in cluttered indoor and outdoor settings. I will close with a discussion of research needs for the next generation of MAVs, including scalable representations for physical AI, active perception with task-level value of information, safe real-time planning under uncertainty, onboard foundation models under size-weight-power constraints, resilient operation with degraded sensing and communication, and verification methods for field-deployed autonomy.

Because AI-enabled robots can be tricked into taking unsafe actions, they require layered, context-aware safety guardrails.

Hard-constraint trajectory planners often rely on commercial solvers and demand substantial computational resources. Existing soft-constraint methods achieve faster computation, but either (1) decouple spatial and temporal optimization or (2) restrict the search space. To overcome these limitations, we introduce MIGHTY, a Hermite spline-based planner that performs spatiotemporal optimization while fully leveraging the continuous search space of a spline. In simulation, MIGHTY achieves a 9.3% reduction in computation time and a 13.1% reduction in travel time over state-of-the-art baselines, with a 100% success rate. In hardware, MIGHTY completes multiple high-speed flights up to 6.7 m/s in a cluttered static environment and long-duration flights with dynamically added obstacles.

Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings. Traditional robot safety approaches do not address the contextual vulnerabilities of LLMs, and current LLM safety approaches overlook the physical risks posed by robots operating in real-world environments. To ensure the safety of LLM-enabled robots, we propose RoboGuard, a two-stage guardrail architecture. RoboGuard first contextualizes pre-defined safety rules by grounding them in the robot's environment using a root-of-trust LLM. This LLM is shielded from malicious prompts and employs chain-of-thought (CoT) reasoning to generate context-dependent safety specifications, such as temporal logic constraints. RoboGuard then resolves conflicts between these contextual safety specifications and potentially unsafe plans using temporal logic control synthesis, ensuring compliance while minimally violating user preferences. In simulation and real-world experiments that consider worst-case jailbreaking attacks, RoboGuard reduces the execution of unsafe plans from over 92% to below 3% without compromising performance on safe plans. We also demonstrate that RoboGuard is resource-efficient, robust against adaptive attacks, and enhanced by its root-of-trust LLM's CoT reasoning. These results demonstrate the potential of RoboGuard to mitigate the safety risks and enhance the reliability of LLM-enabled robots.

This paper develops a real-time decentralized metric-semantic SLAM algorithm that enables a heterogeneous robot team to collaboratively construct object-based metric-semantic maps. The proposed framework integrates a data-driven front-end for instance segmentation from either RGBD cameras or LiDARs and a custom back-end for optimizing robot trajectories and object landmarks in the map. To allow multiple robots to merge their information, we design semantics-driven place recognition algorithms that leverage the informativeness and viewpoint invariance of the object-level metric-semantic map for inter-robot loop closure detection. A communication module is designed to track each robot's observations and those of other robots whenever communication links are available. The framework supports real-time, decentralized operation onboard the robots and has been integrated with three types of aerial and ground platforms. We validate its effectiveness through experiments in both indoor and outdoor environments, as well as benchmarks on public datasets and comparisons with existing methods. The framework is open-sourced and suitable for both single-agent and multi-robot real-time metric-semantic SLAM applications.

Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The problem is challenging in decentralized settings with robots that have limited communication and sensing capabilities. We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem, wherein a convolutional neural network (CNN) processes localized perception; a graph neural network (GNN) facilitates robot communications; finally, a shallow multi-layer perceptron (MLP) computes robot actions. The GNN enables collaboration in the robot swarm by computing what information to communicate with nearby robots and how to incorporate received information. Evaluations show that the LPAC models—trained using imitation learning—outperform standard decentralized and centralized coverage control algorithms. The learned policy generalizes to environments different from the training dataset, transfers to larger environments with more robots, and is robust to noisy position estimates. The results indicate the suitability of LPAC architectures for decentralized navigation in robot swarms to achieve collaborative behavior.

We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximate solutions efficiently. A novel online algorithm performs iterative re-allocation, providing robustness to task failures and model inaccuracies to achieve higher performance than offline approaches. We comprehensively evaluate the algorithms in a testbed with random missions and reward functions and compare them to a mixed- integer solver and a greedy heuristic. Additionally, we validate the overall approach in an advanced simulator, modeling reward functions based on realistic physical phenomena and executing the tasks with realistic robot dynamics. Results establish efficacy in modeling complex missions and efficiency in generating high-fidelity task plans while leveraging task relationships.