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Nakul Gopalan

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15 papers

2 author rows

ICRA Conference 2025 Conference Paper

XMoP: Whole-Body Control Policy for Zero-Shot Cross-Embodiment Neural Motion Planning

Prabin Kumar Rath
Nakul Gopalan

Classical manipulator motion planners work across different robot embodiments [1]. However they plan on a pre-specified static environment representation, and are not scalable to unseen dynamic environments. Neural Motion Planners (NMPs) [2] are an appealing alternative to conventional planners as they incorporate different environmental constraints to learn motion policies directly from raw sensor observations. Contemporary state-of-the-art NMPs can successfully plan across different environments [3]. However none of the existing NMPs generalize across robot embodiments. In this paper we propose Cross-Embodiment Motion Policy (XMoP), a neural policy for learning to plan over a distribution of manipulators. XMoP implicitly learns to satisfy kinematic constraints for a distribution of robots and zero-shot transfers the planning behavior to unseen robotic manipulators within this distribution. We achieve this generalization by formulating a whole-body control policy that is trained on planning demonstrations from over three million procedurally sampled robotic manipulators in different simulated environments. Despite being completely trained on synthetic embodiments and environments, our policy exhibits strong sim-to-real generalization across manipulators with different kinematic variations and degrees of freedom with a single set of frozen policy parameters. We evaluate XMoP on 7 commercial manipulators and show successful cross-embodiment motion planning, achieving an average 70 % success rate on baseline benchmarks. Furthermore, we demonstrate sim-to-real deployment on two unseen manipulators solving novel planning problems across three real-world domains even with dynamic obstacles.

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TMLR Journal 2024 Journal Article

Compositional Instruction Following with Language Models and Reinforcement Learning

Vanya Cohen
Geraud Nangue Tasse
Nakul Gopalan
Steven James
Matthew Gombolay
Ray Mooney
Benjamin Rosman

Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned tasks. To address this, we introduce a novel method: the compositionally-enabled reinforcement learning language agent (CERLLA). Our method reduces the sample complexity of tasks specified with language by leveraging compositional policy representations and a semantic parser trained using reinforcement learning and in-context learning. We evaluate our approach in an environment requiring function approximation and demonstrate compositional generalization to novel tasks. Our method significantly outperforms the previous best non-compositional baseline in terms of sample complexity on 162 tasks designed to test compositional generalization. Our model attains a higher success rate and learns in fewer steps than the non-compositional baseline. It reaches a success rate equal to an oracle policy's upper-bound performance of 92%. With the same number of environment steps, the baseline only reaches a success rate of 80%.

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IROS Conference 2024 Conference Paper

Hardware-Software Co-Design for Path Planning by Drones

Ayushi Dube
Omkar Patil
Gian Singh
Nakul Gopalan
Sarma B. K. Vrudhula

This work consists of two main components: designing a hardware-software co-design, MT +, for adapting the Mikami-Tabuchi algorithm for on-board path planning by drones in a 3D environment; and development of a specialized custom hardware accelerator CDU, as a part of MT +, for parallel collision detection. Collision detection is a performance bottleneck in path planning. MT + reduces the delay in path planning without using any heuristic. A comparative analysis between the state-of-the-art path planning algorithm A* and Mikami-Tabuchi is performed to show that Mikami-Tabuchi is faster than A* in typical real-world environments. In custom-generated environments, path planning using Mikami-Tabuchi shows a latency improvement of 1. 7× across varying average sizes of obstacles and 2. 7× across varying obstacle density over state-of-the-art path planning algorithm, A*. Further, the experiments show that the co-design achieves speedups over a full software implementation on CPU, averaging between 10% to 60% across different densities and sizes of obstacles. CDU area and power overheads are negligible against a conventional single-core processor.

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AAAI Conference 2024 System Paper

Interactive Visual Task Learning for Robots

Weiwei Gu
Anant Sah
Nakul Gopalan

We present a demonstrable framework for robots to learn novel visual concepts and visual tasks via in-situ linguistic interactions with human users. Previous approaches in computer vision have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representations to a concept hierarchy. We extend the approaches that focus on learning visual concept hierarchies and take this ability one step further to demonstrate novel task solving on robots along with the learned visual concepts. To enable a visual concept learner to solve robotics tasks one-shot, we developed two distinct techniques. Firstly, we propose a novel approach, Hi-Viscont(HIerarchical VISual CONcept learner for Task), which augments information of a novel concept, that is being taught, to its parent nodes within a concept hierarchy. This information propagation allows all concepts in a hierarchy to update as novel concepts are taught in a continual learning setting. Secondly, we represent a visual task as a scene graph with language annotations, allowing us to create novel permutations of a demonstrated task zero-shot in-situ. Combining the two techniques, we present a demonstration on a real robot that learns visual task and concepts in one-shot from in-situ interactions with human users, and generalize to perform a novel visual task of the same type in zero-shot. As shown by the studies in the main conference paper, our system achieves a success rate of 50% on solving the whole task correctly with generalization where the baseline performs at 14% without any ability to generalize to novel tasks and concepts. We will demonstrate our working interactive learning pipeline at AAAI 2024 in person with our robot and other required hardware.

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AAAI Conference 2024 Conference Paper

Interactive Visual Task Learning for Robots

Weiwei Gu
Anant Sah
Nakul Gopalan

We present a framework for robots to learn novel visual concepts and tasks via in-situ linguistic interactions with human users. Previous approaches have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representations to a concept hierarchy. We extend the approaches that focus on learning visual concept hierarchies by enabling them to learn novel concepts and solve unseen robotics tasks with them. To enable a visual concept learner to solve robotics tasks one-shot, we developed two distinct techniques. Firstly, we propose a novel approach, Hi-Viscont(HIerarchical VISual CONcept learner for Task), which augments information of a novel concept to its parent nodes within a concept hierarchy. This information propagation allows all concepts in a hierarchy to update as novel concepts are taught in a continual learning setting. Secondly, we represent a visual task as a scene graph with language annotations, allowing us to create novel permutations of a demonstrated task zero-shot in-situ. We present two sets of results. Firstly, we compare Hi-Viscont with the baseline model (FALCON) on visual question answering(VQA) in three domains. While being comparable to the baseline model on leaf level concepts, Hi-Viscont achieves an improvement of over 9% on non-leaf concepts on average. Secondly, we conduct a human-subjects experiment where users teach our robot visual tasks in-situ. We compare our model’s performance against the baseline FALCON model. Our framework achieves 33% improvements in success rate metric, and 19% improvements in the object level accuracy compared to the baseline model. With both of these results we demonstrate the ability of our model to learn tasks and concepts in a continual learning setting on the robot.

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IROS Conference 2024 Conference Paper

Learning Temporally Composable Task Segmentations with Language

Divyanshu Raj
Omkar Patil
Weiwei Gu
Chitta Baral
Nakul Gopalan

In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory with the supervision provided by language instructions. Learning longer horizon tasks is challenging with techniques such as reinforcement learning and behavior cloning. Previous approaches have split these long tasks into shorter tasks that are easier to learn by using statistical change point detection methods. However, classical changepoint detection methods function only with low dimensional robot trajectory data and not with high dimensional inputs such as vision. Our goal in this work is to split longer horizon tasks, represented by trajectories into shorter horizon tasks that can be learned using conventional behavior cloning approaches using guidance from language. In our approach we use techniques from the video moment retrieval problem on robot trajectory data to demonstrate a high-dimensional generalizable change-point detection approach. Our proposed moment retrieval-based approach shows a more than 30% improvement in mean average precision (mAP) for identifying trajectory sub-tasks with language guidance compared to that without language. We perform ablations to understand the effects of domain randomization, sample complexity, views, and sim-to-real transfer of our method. In our data ablation we find that just with a 100 labelled trajectories we can achieve a 61. 41 mAP, demonstrating the sample efficiency of using such an approach. Further, behavior cloning models trained on our segmented trajectories outperform a single model trained on the whole trajectory by up to 20%.

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IROS Conference 2023 Conference Paper

Improved Inference of Human Intent by Combining Plan Recognition and Language Feedback

Ifrah Idrees
Tian Yun 0001
Naveen Sharma
Yunxin Deng
Nakul Gopalan
George Konidaris 0001
Stefanie Tellex

Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with people effectively, robots must recognize human plans and goals from noisy observations of human actions, even when the user acts sub-optimally. Previous works on Plan and Goal Recognition (PGR) as planning have used hierarchical task networks (HTN) to model the actor/human. However, these techniques are insufficient as they do not have user engagement via natural modes of interaction such as language. Moreover, they have no mechanisms to let users, especially those with cognitive impairments, know of a deviation from their original plan or about any sub-optimal actions taken towards their goal. We propose a novel framework for plan and goal recognition in partially observable domains—Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its belief in human progress by asking clarification questions about noisy sensor data and sub-optimal human actions. We evaluate the performance of D4GR over two simulated domains—kitchen and blocks domain. With language feedback and the world state information in a hierarchical task model, we show that D4GR framework for the highest sensor noise performs 1% better than HTN in goal accuracy in both domains. For plan accuracy, D4GR outperforms by 4% in the kitchen domain and 2% in the blocks domain in comparison to HTN. The ALWAYS-ASK oracle outperforms our policy by 3% in goal recognition and 7% in plan recognition. D4GR does so by asking 68% fewer questions than an oracle baseline. We also demonstrate a real-world robot scenario in the kitchen domain, validating the improved plan and goal recognition of D4GR in a realistic setting.

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IROS Conference 2020 Conference Paper

Building Plannable Representations with Mixed Reality

Eric Rosen
Nishanth Kumar
Nakul Gopalan
Daniel Ullman 0002
George Konidaris 0001
Stefanie Tellex

We propose Action-Oriented Semantic Maps (AOSMs), a representation that enables a robot to acquire object manipulation behaviors and semantic information about the environment from a human teacher with a Mixed Reality Head-Mounted Display (MR-HMD). AOSMs are a representation that captures both: a) high-level object manipulation actions in an object class's local frame, and b) semantic representations of objects in the robot's global map that are grounded for navigation. Humans can use a MR-HMD to teach the agent the information necessary for planning object manipulation and navigation actions by interacting with virtual 3D meshes overlaid on the physical workspace. We demonstrate that our system enables users to quickly and accurately teach a robot the knowledge required to autonomously plan and execute three household tasks: picking up a bottle and throwing it in the trash, closing a sink faucet, and flipping a light switch off.

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ICRA Conference 2019 Conference Paper

Flight, Camera, Action! Using Natural Language and Mixed Reality to Control a Drone

Baichuan Huang
Deniz Bayazit
Daniel Ullman 0002
Nakul Gopalan
Stefanie Tellex

With increasing autonomy, robots like drones are increasingly accessible to untrained users. Most users control drones using a low-level interface, such as a radio-controlled (RC) controller. For a wider adoption of these technologies by the public, a much higher-level interface, such as natural language or mixed reality (MR), allows the automation of the control of the agent in a goal-oriented setting. We present an interface that uses natural language grounding within an MR environment to solve high-level task and navigational instructions given to an autonomous drone. To the best of our knowledge, this is the first work to perform fully autonomous language grounding in an MR setting for a robot. Given a map, our interface first grounds natural language commands to reward specifications within a Markov Decision Process (MDP) framework. Then, it passes the reward specification to an MDP solver. Finally, the drone performs the desired operations in the real world while planning and localizing itself. Our approach uses MR to provide a set of known virtual landmarks, enabling the drone to understand commands referring to objects without being equipped with object detectors for multiple novel objects or a predefined environment model. We conducted an exploratory user study to assess users' experience of our MR interface with and without natural language, as compared to a web interface. We found that users were able to command the drone more quickly via both MR interfaces as compared to the web interface, with roughly equal system usability scores across all three interfaces.

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IROS Conference 2019 Conference Paper

Grounding Language Attributes to Objects using Bayesian Eigenobjects

Vanya Cohen
Benjamin Burchfiel
Thao Nguyen
Nakul Gopalan
Stefanie Tellex
George Konidaris 0001

We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the object described by the phrase. Our system is designed to learn from only a small amount of human-labeled language data and generalize to viewpoints not represented in the language-annotated depth image training set. By decoupling 3D shape representation from language representation, this method is able to ground language to novel objects using a small amount of language-annotated depth-data and a larger corpus of unlabeled 3D object meshes, even when these objects are partially observed from unusual viewpoints. Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions. Our method also enables viewpoint transfer; trained on human-annotated data on a small set of depth images captured from frontal viewpoints, our system successfully predicted object attributes from rear views despite having no such depth images in its training set. Finally, we demonstrate our approach on a Baxter robot, enabling it to pick specific objects based on human-provided natural language descriptions.

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ICRA Conference 2018 Conference Paper

Learning to Parse Natural Language to Grounded Reward Functions with Weak Supervision

Edward C. Williams
Nakul Gopalan
Mina Rhee
Stefanie Tellex

In order to intuitively and efficiently collaborate with humans, robots must learn to complete tasks specified using natural language. We represent natural language instructions as goal-state reward functions specified using lambda calculus. Using reward functions as language representations allows robots to plan efficiently in stochastic environments. To map sentences to such reward functions, we learn a weighted linear Combinatory Categorial Grammar (CCG) semantic parser. The parser, including both parameters and the CCG lexicon, is learned from a validation procedure that does not require execution of a planner, annotating reward functions, or labeling parse trees, unlike prior approaches. To learn a CCG lexicon and parse weights, we use coarse lexical generation and validation-driven perceptron weight updates using the approach of Artzi and Zettlemoyer [4]. We present results on the Cleanup World domain [18] to demonstrate the potential of our approach. We report an F1 score of 0. 82 on a collected corpus of 23 tasks containing combinations of nested referential expressions, comparators and object properties with 2037 corresponding sentences. Our goal-condition learning approach enables an improvement of orders of magnitude in computation time over a baseline that performs planning during learning, while achieving comparable results. Further, we conduct an experiment with just 6 labeled demonstrations to show the ease of teaching a robot behaviors using our method. We show that parsing models learned from small data sets can generalize to commands not seen during training.

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RLDM Conference 2017 Conference Abstract

Generalized Inverse Reinforcement Learning

Nakul Gopalan
Amy Greenwald
Michael Littman
James MacGlashan

Inverse Reinforcement Learning (IRL) is used to teach behaviors to agents, by having them learn a reward function from example trajectories. The underlying assumption is usually that these trajectories represent optimal behavior. However, it is not always possible for a user to provide examples of optimal trajectories. This problem has been tackled previously by labeling trajectories with a score that indicates good and bad behaviors. In this work, we formalize the IRL problem in a generalized framework that allows for learning from failed demonstrations. In our framework, users can score entire trajectories as well as individual state-action pairs. This allows the agent to learn preferred behaviors from a relatively small number of trajectories. We expect this framework to be especially useful in robotics domains, where the user can collect fewer trajectories at the cost of labeling bad state-action pairs, which might be easier than maneuvering a robot to collect additional (entire) trajectories.

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ICAPS Conference 2017 Conference Paper

Planning with Abstract Markov Decision Processes

Nakul Gopalan
Marie desJardins
Michael L. Littman
James MacGlashan
Shawn Squire
Stefanie Tellex
John Winder
Lawson L. S. Wong

Robots acting in human-scale environments must plan under uncertainty in large state–action spaces and face constantly changing reward functions as requirements and goals change. Planning under uncertainty in large state–action spaces requires hierarchical abstraction for efficient computation. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level “flat” MDP. AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct. We present empirical results showing significantly improved planning speed, while maintaining solution quality, in the Taxi domain and in a mobile-manipulation robotics problem. Furthermore, our approach allows specification of a decision-making model for a mobile-manipulation problem on a Turtlebot, spanning from low-level control actions operating on continuous variables all the way up through high-level object manipulation tasks.

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RLDM Conference 2017 Conference Abstract

Planning with Abstract Markov Decision Processes

Nakul Gopalan
Michael Littman
Shawn Squire
Stefanie Tellex
John Winder
Lawson Wong

Robots acting in human-scale environments must plan under uncertainty in large state–action spaces and face constantly changing reward functions as requirements and goals change. Planning un- der uncertainty in large state–action spaces requires hierarchical abstraction for efficient computation. We (Gopalan et al. 2017 In Press) introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level “flat” MDP. AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct. We present empirical results showing significantly improved planning speed, while maintaining solution quality, in the Taxi domain and in a mobile-manipulation robotics prob- lem. Furthermore, our approach allows specification of a decision-making model for a mobile-manipulation problem on a Turtlebot, spanning from low-level control actions operating on continuous variables all the way up through high-level object manipulation tasks.

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ICRA Conference 2013 Conference Paper

Feedback error learning for rhythmic motor primitives

Nakul Gopalan
Marc Peter Deisenroth
Jan Peters 0001

Rhythmic motor primitives can be used to learn a variety of oscillatory behaviors from demonstrations or reward signals, e. g. , hopping, walking, running and ball-bouncing. However, frequently, such rhythmic motor primitives lead to failures unless a stabilizing controller ensures their functionality, e. g. , a balance controller for a walking gait. As an ideal oscillatory behavior requires the stabilizing controller only for exceptions, e. g. , to prevent failures, we devise an online learning approach that reduces the dependence on the stabilizing controller. Inspired by related approaches in model learning, we employ the stabilizing controller's output as a feedback error learning signal for adapting the gait. We demonstrate the resulting approach in two scenarios: a rhythmic arm's movements and gait adaptation of an underactuated biped.

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