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Ajay Shankar

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6 papers

2 author rows

IROS Conference 2025 Conference Paper

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

Yuhao Zhang
Keisuke Okumura 0001
Heedo Woo
Ajay Shankar
Amanda Prorok

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors.

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ICRA Conference 2025 Conference Paper

Language-Conditioned Offline RL for Multi-Robot Navigation

Steven D. Morad
Ajay Shankar
Jan Blumenkamp
Amanda Prorok

We present a method for synthesizing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from pretrained Large Language Models (LLMs), and train them via offline reinforcement learning with as little as 20 minutes of randomly-collected real-world data. Experiments on a team of five real robots show that these policies generalize well to unseen commands, indicating an understanding of the LLM latent space. Our method requires no simulators or environment models, and produces low-latency control policies that can be deployed directly to real robots without finetuning. We provide videos of our experiments at https://sites.google.com/view/llm-marl.

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JMLR Journal 2025 Journal Article

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

Matteo Bettini
Ajay Shankar
Amanda Prorok

Evolutionary science provides evidence that diversity confers resilience in natural systems. Yet, traditional multi-agent reinforcement learning techniques commonly enforce homogeneity to increase training sample efficiency. When a system of learning agents is not constrained to homogeneous policies, individuals may develop diverse behaviors, resulting in emergent complementarity that benefits the system. Despite this, there is a surprising lack of tools that quantify behavioral diversity. Such techniques would pave the way towards understanding the impact of diversity in collective artificial intelligence and enabling its control. In this paper, we introduce System Neural Diversity (SND): a measure of behavioral heterogeneity in multi-agent systems. We discuss and prove its theoretical properties, and compare it with alternate, state-of-the-art behavioral diversity metrics used in the robotics domain. Through simulations of a variety of cooperative multi-robot tasks, we show how our metric constitutes an important tool that enables measurement and control of behavioral heterogeneity. In dynamic tasks, where the problem is affected by repeated disturbances during training, we show that SND allows us to measure latent resilience skills acquired by the agents, while other proxies, such as task performance (reward), fail to. Finally, we show how the metric can be employed to control diversity, allowing us to enforce a desired heterogeneity set-point or range. We demonstrate how this paradigm can be used to bootstrap the exploration phase, finding optimal policies faster, thus enabling novel and more efficient MARL paradigms. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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AAMAS Conference 2023 Conference Paper

Heterogeneous Multi-Robot Reinforcement Learning

Matteo Bettini
Ajay Shankar
Amanda Prorok

Cooperative multi-robot tasks can benefit from heterogeneity in the robots’ physical and behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) frameworks lack the ability to explicitly accommodate policy heterogeneity, and typically constrain agents to share neural network parameters. This enforced homogeneity limits application in cases where the tasks benefit from heterogeneous behaviors. In this paper, we crystallize the role of heterogeneity in MARL policies. Towards this end, we introduce Heterogeneous Graph Neural Network Proximal Policy Optimization (HetGPPO), a paradigm for training heterogeneous MARL policies that leverages a Graph Neural Network for differentiable inter-agent communication. HetGPPO allows communicating agents to learn heterogeneous behaviors while enabling fully decentralized training in partially observable environments. We complement this with a taxonomical overview that exposes more heterogeneity classes than previously identified. To motivate the need for our model, we present a characterization of techniques that homogeneous models can leverage to emulate heterogeneous behavior, and show how this “apparent heterogeneity” is brittle in real-world conditions. Through simulations and real-world experiments, we show that: (i) when homogeneous methods fail due to strong heterogeneous requirements, HetGPPO succeeds, and, (ii) when homogeneous methods are able to learn apparently heterogeneous behaviors, HetGPPO achieves higher resilience to both training and deployment noise.

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ICRA Conference 2021 Conference Paper

Freyja: A Full Multirotor System for Agile & Precise Outdoor Flights

Ajay Shankar
Sebastian G. Elbaum
Carrick Detweiler

Several independent approaches exist for state estimation and control of multirotor unmanned aerial systems (UASs) that address specific and constrained operational conditions. This work presents a complete end-to-end pipeline that enables precise, aggressive and agile maneuvers for multirotor UASs under real and challenging outdoor environments. We leverage state-of-the-art optimal methods from the literature for trajectory planning and control, such that designing and executing dynamic paths is fast, robust and easy to customize for a particular application. The complete pipeline, built entirely using commercially available components, is made open-source and fully documented to facilitate adoption. We demonstrate its performance in a variety of operational settings, such as hovering at a spot under dynamic wind speeds of up to 5– 6m/s (12–15mi/h) while staying within 12cm of 3D error. We also characterize its capabilities in flying high-speed trajectories outdoors, and enabling fast aerial docking with a moving target with planning and interception occurring in under 8s.

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IROS Conference 2018 Conference Paper

Towards Aerial Recovery of Parachute-Deployed Payloads

Ajay Shankar
Sebastian G. Elbaum
Carrick Detweiler

Sensor payloads suspended from parachutes are often used in atmospheric profiling applications. They drift freely and often end up landing in inaccessible regions that make their retrieval challenging or impossible. In this paper, we develop and evaluate an approach using a multirotor unmanned aerial system to autonomously retrieve the parachute while it is still in the air. The system relies only on the initial conditions of the parachute-payload system and feedback from the vehicle's onboard cameras to track and then intercept the parachute mid-air in under 40 seconds on average. We present the results from our field experiments where we demonstrate the feasibility of the system and discuss its applicability to long-term payload transportation systems.

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