We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM), and categorizes the instructions into navigation and behavioral guidelines. Navigation guidelines consist of directional commands (e.g., “move forward until“) and associated landmarks (e.g., “the building with blue windows”), while behavioral guidelines encompass regulatory actions (e.g., “stay on“) and their corresponding objects (e.g., “pavements“). We use VLMs for their zero-shot scene understanding capabilities to estimate landmark locations from RGB images for robot navigation. Further, we introduce a novel scene representation that utilizes VLMs to ground behavioral rules into a behavioral cost map. This cost map encodes the presence of behavioral objects within the scene and assigns costs based on their regulatory actions. The behavioral cost map is integrated with a LiDAR-based occupancy map for navigation. To navigate outdoor scenes while adhering to the instructed behaviors, we present an unconstrained Model Predictive Control (MPC)based planner that prioritizes both reaching landmarks and following behavioral guidelines. We evaluate the performance of BehAV on a quadruped robot across diverse real-world scenarios, demonstrating a 22.49 % improvement in alignment with human-teleoperated actions, as measured by Fréchet distance, and achieving a 40 % higher navigation success rate compared to state-of-the-art methods.

In this paper, we present a probabilistic and unconstrained model predictive control formulation for robot navigation under uncertainty. We present (1) a closed-form approximation of the probability of collision that naturally models the propagation of uncertainty over the planning horizon and is computationally cheap to evaluate, and (2) a collision-cost formulation which provably preserves forward invariance (i.e., keeps the robot away from obstacles) when combined with the probability formulation. Notably, our formulation avoids hard constraints by construction, which in turn avoids abrupt transitions in robot behavior around the constraint boundaries ensuring graceful navigation. Further, we present proof for the forward invariance and the stability of the approach. We compare the efficacy of our method with the baseline [1], which the proposed approach builds on. We demonstrate that the approach results in confident and safe robot navigation in tight spaces by smoothly slowing down the robot in low survivability environments (e.g., tight corridors), but also allows it to move away from obstacles safely when needed.

Decentralized navigation methods rely primarily on local observations, lacking the global awareness needed to coordinate effectively within a multi-agent system. Exchanging relevant messages between agents can promote cooperation and improve navigation efficiency. We present a Reinforcement Learning (RL)-based decentralized navigation approach that learns ‘when,’ ‘what,’ and ‘with whom’ to communicate for safe and cooperative navigation. Our method leverages a visual transformer and self-attention mechanism to encode the local occupancy map and the state information of neighbors into fixed-length encodings, allowing it to handle an arbitrary number of neighbors for collision-free navigation. In addition, the network encodes the agent’s state information and observations of neighboring agents into a concise message vector by learning what information is crucial to communicate, which is shared with neighboring agents upon request. Moreover, to avoid indiscriminate broadcasting, the network learns when and with whom to communicate and request message vectors. Subsequently, the messages communicated alongside the local information are used to guide navigation decisions. We evaluate our method against state-of-the-art baselines in complex scenarios, including narrow corridors and environments with multiple agents. We observe considerable improvements in terms of navigation performance, showing up to ∼ 2× improvement in navigation success rates and a reduction of up to ∼ 20% in path length.

We present VLPG-Nav, a visual language navigation method for guiding robots to specified objects within household scenes. Unlike existing methods primarily focused on navigating the robot toward objects, our approach considers the additional challenge of centering the object within the robot’s camera view. Our method builds a visual language pose graph (VLPG) that functions as a spatial map of VL embeddings. Given an open-vocabulary object query, we plan a viewpoint for object navigation using the VLPG. Despite navigating to the viewpoint, real-world challenges such as object occlusion, displacement, and the robot’s localization errors can prevent visibility. We build an object localization probability map that leverages the robot’s current observations and prior VLPG. When the object is not visible, the probability map is updated, and an alternate viewpoint is computed. In addition, we propose an object-centering formulation that locally adjusts the robot’s pose to center the object in the camera view. We evaluate the effectiveness of our approach through simulations and real-world experiments, evaluating its ability to successfully view and center the object within the camera’s field of view. VLPG-Nav demonstrates improved performance in locating the object, navigating around occlusions, and centering the object within the robot’s camera view, outperforming selected baselines in the evaluation settings.

We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significantly reduces conservative and deadlocking behaviors and generates smooth trajectories. In particular, we propose a new collision probability function that effectively captures the risk associated with a given configuration and the time to avoid collisions based on the velocity direction. Moreover, we propose a terminal state cost based on the expected time-to-goal and time-to-collision values that helps in avoiding trajectories that could result in deadlock. We evaluate our cost formulation in multiple simulated scenarios, including narrow corridors with dynamic obstacles, and observe significantly improved navigation behavior and reduced deadlocks as compared to prior methods.

We present a novel reinforcement learning (RL) based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. Our approach is designed for scenarios in which multiple robots are used to perform various pick up and delivery tasks. We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it. At the higher level, we solve the task allocation by formulating it in terms of Markov Decision Processes and choosing the appropriate rewards to minimize the Total Travel Delay (TTD). At the lower level, we use a decentralized navigation scheme based on ORCA that enables each robot to perform these tasks in an independent manner, and avoid collisions with other robots and dynamic obstacles. We combine these lower and upper levels by defining rewards for the higher level as the feedback from the lower level navigation algorithm. We perform extensive evaluation in complex warehouse layouts with large number of agents and highlight the benefits over state-of-the-art algorithms based on myopic pickup distance minimization and regret-based task selection. We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.

We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in scenarios with multiple corridors. To handle dense scenes with narrow passages, our approach computes the initial path for each agent to its assigned goal using a lattice planner. Based on neighbors' information, each agent performs online replanning using a congestion metric that tends to reduce the collisions and improves the navigation performance. Furthermore, we use the Voronoi cells of each agent to plan the local motion as well as a corridor selection strategy to limit the congestion in narrow passages. We evaluate the performance of our approach in complex scenes with tens of agents and narrow passages. We show that our Coupled Global-Local approach and Replanning (CGLR) improves the performance and efficiency over prior decentralized methods. In addition, our approach results in a higher success rate in terms of collision-free navigation to the goals, showing improvement in the range of 3-70% over prior decentralized solutions in certain scenarios.

An important aspect of dynamic urban coverage is how building collision avoidance is incorporated into the overall coverage mission. We consider a multi-agent urban dynamic coverage problem in which a team of flying agents uses downward facing cameras to observe the street-level environment outside of buildings. Cameras are assumed to be ineffective above a maximum altitude (lower than building height), such that agents must move around or over buildings to complete their mission. The main objective of this paper is to compare three different building avoidance strategies that are compatible with dynamic ergodic methods. To provide context for these results, we also compare our results to three other common coverage methods including: boustrophedon coverage (lawn-mower sweep), Voronoi region based coverage, and a naive grid method. All algorithms are evaluated in simulation with respect to four performance metrics (percent coverage, revisit count, revisit time, and the integral of area viewed over time), across team sizes ranging from 1 to 25 agents, and in five types of urban environments of varying density and height. We find that the relative performance of algorithms changes based on the ratio of team size to search area, as well the height and density characteristics of the urban environment.

We present a decentralized collision avoidance method for dense environments based on buffered Voronoi cells (BVC) and reciprocal velocity obstacles (RVO). Our approach is designed for scenarios with a large number of agents in close proximity and provides passive-friendly collision avoidance guarantees. The Voronoi cells are superimposed with RVO cones to compute a suitable direction for each agent, and we use that direction to compute a local collision-free path. Our approach can also satisfy double-integrator dynamics, and we use the properties of the BVC to formulate a simple, decentralized deadlock resolution strategy. We demonstrate the benefits of V-RVO in complex scenarios with tens of agents in close proximity. In practice, V-RVO’s performance is comparable to prior velocity-obstacle methods, and the collision avoidance behavior is significantly less conservative than ORCA.