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Haresh Karnan

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11 papers

2 author rows

IROS Conference 2025 Conference Paper

Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds

Rohan Chandra
Haresh Karnan
Negar Mehr
Peter Stone 0001
Joydeep Biswas

Social robot navigation in crowded public spaces such as university campuses, restaurants, grocery stores, and hospitals, is an increasingly important area of research. One of the core strategies for achieving this goal is to understand humans’ intent–underlying psychological factors that govern their motion–by learning how humans assign rewards to their actions, typically via inverse reinforcement learning (IRL). Despite significant progress in IRL, learning reward functions of multiple agents simultaneously in dense unstructured pedestrian crowds has remained intractable due to the nature of the tightly coupled social interactions that occur in these scenarios e. g. passing, intersections, swerving, weaving, etc. In this paper, we present a new multi-agent maximum entropy inverse reinforcement learning algorithm for real world unstructured pedestrian crowds. Key to our approach is a simple, but effective, mathematical trick which we name the so-called "tractability-rationality trade-off" trick that achieves tractability at the cost of a slight reduction in accuracy. We compare our approach to the classical single-agent MaxEnt IRL as well as state-of-the-art trajectory prediction methods on several datasets including the ETH, UCY, SCAND, JRDB, and a new dataset, called Speedway, collected at a busy intersection on a University campus focusing on dense, complex agent interactions. Our key findings show that, on the dense Speedway dataset, our approach ranks 1 st among top 7 baselines with > 2× improvement over single-agent IRL, and is competitive with state-of-the-art large transformer-based encoder-decoder models on sparser datasets such as ETH/UCY (ranks 3 rd among top 7 baselines).

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ICRA Conference 2024 Conference Paper

Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds

Amir Hossain Raj
Zichao Hu
Haresh Karnan
Rohan Chandra
Amirreza Payandeh
Luisa Mao
Peter Stone 0001
Joydeep Biswas

Empowering robots to navigate in a socially compliant manner is essential for the acceptance of robots moving in human-inhabited environments. Previously, roboticists have developed geometric navigation systems with decades of empirical validation to achieve safety and efficiency. However, the many complex factors of social compliance make geometric navigation systems hard to adapt to social situations, where no amount of tuning enables them to be both safe (people are too unpredictable) and efficient (the frozen robot problem). With recent advances in deep learning approaches, the common reaction has been to entirely discard these classical navigation systems and start from scratch, building a completely new learning-based social navigation planner. In this work, we find that this reaction is unnecessarily extreme: using a large-scale real-world social navigation dataset, SCAND, we find that geometric systems can produce trajectory plans that align with the human demonstrations in a large number of social situations. We, therefore, ask if we can rethink the social robot navigation problem by leveraging the advantages of both geometric and learning-based methods. We validate this hybrid paradigm through a proof-of-concept experiment, in which we develop a hybrid planner that switches between geometric and learning-based planning. Our experiments on both SCAND and two physical robots show that the hybrid planner can achieve better social compliance compared to using either the geometric or learning-based approach alone.

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ICRA Conference 2024 Conference Paper

Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference-Aligned Path Planning

Haresh Karnan
Elvin Yang
Garrett Warnell
Joydeep Biswas
Peter Stone 0001

Autonomous mobility tasks such as last-mile delivery require reasoning about operator-indicated preferences over terrains on which the robot should navigate to ensure both robot safety and mission success. However, coping with out of distribution data from novel terrains or appearance changes due to lighting variations remains a fundamental problem in visual terrain-adaptive navigation. Existing solutions either require labor-intensive manual data re-collection and labeling or use hand-coded reward functions that may not align with operator preferences. In this work, we posit that operator preferences for visually novel terrains, which the robot should adhere to, can often be extrapolated from established terrain preferences within the inertial-proprioceptive-tactile domain. Leveraging this insight, we introduce Preference extrApolation for Terrain-awarE Robot Navigation (PATERN), a novel framework for extrapolating operator terrain preferences for visual navigation. PATERN learns to map inertial-proprioceptive-tactile measurements from the robot’s observations to a representation space and performs nearest-neighbor search in this space to estimate operator preferences over novel terrains. Through physical robot experiments in outdoor environments, we assess PATERN’s capability to extrapolate preferences and generalize to novel terrains and challenging lighting conditions. Compared to baseline approaches, our findings indicate that PATERN 1 robustly generalizes to diverse terrains and varied lighting conditions, while navigating in a preference-aligned manner.

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ICRA Conference 2022 Conference Paper

Adversarial Imitation Learning from Video Using a State Observer

Haresh Karnan
Faraz Torabi
Garrett Warnell
Peter Stone 0001

The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone. However, current state-of-the-art approaches developed for this problem exhibit high sample complexity due, in part, to the high-dimensional nature of video observations. Towards addressing this issue, we introduce here a new algorithm called Visual Generative Adversarial Imitation from Observation using a State Observer (VGAIfO-SO). At its core, VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised state observer, which provides estimates of lower-dimensional proprioceptive state representations from high-dimensional images. We show experimentally in several continuous control environments that VGAIfO-SO is more sample efficient than other IfO algorithms at learning from video-only demonstrations and can sometimes even achieve performance close to the Generative Adversarial Imitation from Observation (GAIfO) algorithm that has privileged access to the demonstrator's proprioceptive state information.

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IROS Conference 2022 Conference Paper

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization

Pranav Atreya
Haresh Karnan
Kavan Singh Sikand
Xuesu Xiao
Sadegh Rabiee
Joydeep Biswas

Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.

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IROS Conference 2022 Conference Paper

VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics

Haresh Karnan
Kavan Singh Sikand
Pranav Atreya
Sadegh Rabiee
Xuesu Xiao
Garrett Warnell
Peter Stone 0001
Joydeep Biswas

One of the key challenges in high-speed off-road navigation on ground vehicles is that the kinodynamics of the vehicle-terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future. To this end, we introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based IKD model that is conditioned on visual information from a terrain patch ahead of the robot in addition to past inertial information, enabling it to anticipate kinodynamic interactions in the future. We validate the effectiveness of VI-IKD in accurate high-speed off-road navigation experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both indoor and outdoor environments and show that compared to other state-of-the-art approaches, VI-IKD enables more accurate and robust off-road navigation on a variety of different terrains at speeds of up to 3. 5m/s.

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ICRA Conference 2022 Conference Paper

VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation

Haresh Karnan
Garrett Warnell
Xuesu Xiao
Peter Stone 0001

While imitation learning for vision-based au-tonomous mobile robot navigation has recently received a great deal of attention in the research community, existing approaches typically require state-action demonstrations that were gathered using the deployment platform. However, what if one cannot easily outfit their platform to record these demonstration signals or-worse yet-the demonstrator does not have access to the platform at all? Is imitation learning for vision-based autonomous navigation even possible in such scenarios? In this work, we hypothesize that the answer is yes and that recent ideas from the Imitation from Observation (IfO) literature can be brought to bear such that a robot can learn to navigate using only ego-centric video collected by a demonstrator, even in the presence of viewpoint mismatch. To this end, we introduce a new algorithm, Visual-Observation-only Imitation Learning for Autonomous navigation (VOILA), that can successfully learn navigation policies from a single video demonstration collected from a physically different agent. We evaluate VOILA in the AirSim simulator and show that VOILA not only successfully imitates the expert, but that it also learns navigation policies that can generalize to novel environments. Further, we demonstrate the effectiveness of VOILA in a real-world setting by showing that it allows a wheeled Jackal robot to successfully imitate a human walking in an environment while recording video with a handheld mobile phone camera.

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NeurIPS Conference 2020 Conference Paper

An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch

Siddharth Desai
Ishan Durugkar
Haresh Karnan
Garrett Warnell
Josiah Hanna
Peter Stone

We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to reduce the amount of interaction with the target environment during learning. This problem is particularly important in sim-to-real transfer because simulators inevitably model real-world dynamics imperfectly. In this paper, we show that one existing solution to this transfer problem-- grounded action transformation --is closely related to the problem of imitation from observation (IfO): learning behaviors that mimic the observations of behavior demonstrations. After establishing this relationship, we hypothesize that recent state-of-the-art approaches from the IfO literature can be effectively repurposed for grounded transfer learning. To validate our hypothesis we derive a new algorithm -- generative adversarial reinforced action transformation (GARAT) -- based on adversarial imitation from observation techniques. We run experiments in several domains with mismatched dynamics, and find that agents trained with GARAT achieve higher returns in the target environment compared to existing black-box transfer methods.

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IROS Conference 2020 Conference Paper

Reinforced Grounded Action Transformation for Sim-to-Real Transfer

Haresh Karnan
Siddharth Desai
Josiah P. Hanna
Garrett Warnell
Peter Stone 0001

Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the "reality gap"). Some existing solutions to this sim-to-real problem, such as Grounded Action Transformation (gat), use a small amount of real-world experience to minimize the reality gap by "grounding" the simulator. While very effective in certain scenarios, gat is not robust on problems that use complex function approximation techniques to model a policy. In this paper, we introduce Reinforced Grounded Action Transformation (rgat), a new sim-to-real technique that uses Reinforcement Learning (RL) not only to update the target policy in simulation, but also to perform the grounding step itself. This novel formulation allows for end-to-end training during the grounding step, which, compared to gat, produces a better grounded simulator. Moreover, we show experimentally in several MuJoCo domains that our approach leads to successful transfer for policies modeled using neural networks.

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IROS Conference 2020 Conference Paper

Stochastic Grounded Action Transformation for Robot Learning in Simulation

Siddharth Desai
Haresh Karnan
Josiah P. Hanna
Garrett Warnell
Peter Stone 0001

Robot control policies learned in simulation do not often transfer well to the real world. Many existing solutions to this sim-to-real problem, such as the Grounded Action Transformation (GAT) algorithm, seek to correct for- or ground-these differences by matching the simulator to the real world. However, the efficacy of these approaches is limited if they do not explicitly account for stochasticity in the target environment. In this work, we analyze the problems associated with grounding a deterministic simulator in a stochastic real world environment, and we present examples where GAT fails to transfer a good policy due to stochastic transitions in the target domain. In response, we introduce the Stochastic Grounded Action Transformation (SGAT) algorithm, which models this stochasticity when grounding the simulator. We find experimentally-for both simulated and physical target domains-that SGAT can find policies that are robust to stochasticity in the target domain.

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IROS Conference 2017 Conference Paper

Visual feedback control of tensegrity robotic systems

Haresh Karnan
Raman Goyal
Manoranjan Majji
Robert E. Skelton
Puneet Singla

Feedback control problems pertaining to the control of tensegrity robotic systems are detailed in this paper. The unique problems that arise due to the positivity of the string tensions required to maintain the static stability and desirable stiffness of the structural system are shown to bring about interesting opportunities to optimize for the redundancy in the actuation process. The static stability consideration, coupled with the nonlinear dynamics and the sensor models introduce additional algebraic constraints in implementation of both kinematic and model based dynamic controllers for tensegrity systems. Approaches to develop kinematic and dynamic control techniques are detailed in this paper. A bench top experimental setup consisting of a simple tensegrity system is utilized to demonstrate the efficacy of the output feedback control approach developed in the paper. Near real time image measurements are utilized to drive the output error used in the control scheme.

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