GLiDE: Grounding Language Plans in Demonstrations through Counterfactual Perturbations

Yanwei Wang     Tsun-Hsuan Wang     Jiayuan Mao     Michael Hagenow     Julie Shah
Interactive Robotics Lab / CSAIL
Massachusetts Institute of Technology


Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI. Whereas prior works have focused on leveraging LLMs directly for planning in symbolic spaces, this work uses LLMs to guide the search of task structures and constraints implicit in multi-step demonstrations. Specifically, we borrow from manipulation planning literature the concept of mode families, which group robot configurations by specific motion constraints, to serve as an abstraction layer between the high-level language representations of an LLM and the low-level physical trajectories of a robot. By replaying a few human demonstrations with synthetic perturbations, we generate coverage over the demonstrations' state space with additional successful executions as well as counterfactuals that fail the task. Our explanation-based learning framework trains an end-to-end differentiable neural network to predict successful trajectories from failures and as a by-product learns classifiers that ground low-level states and images in mode families without dense labeling. The learned grounding classifiers can further be used to translate language plans into reactive policies in the physical domain in an interpretable manner. We show our approach improves the interpretability and reactivity of imitation learning through 2D navigation and simulated and real robot manipulation tasks.


Grounding Language Plans in Demonstrations through Counterfactual Perturbations
Yanwei Wang, Tsun-Hsuan Wang, Jiayuan Mao, Michael Hagenow, Julie Shah
arxiv / review / code / MIT News / techcrunch
ICLR 2024 (Spotlight, acceptance rate: 5%)



By prompting LLM to describe successful executions in terms of valid mode transitions via a feasibility matrix, and augmenting demonstrations with counterfactual perturbations, we train a fully differentiable pipeline to predict task execution outcomes. Since the fully-differentiable pipeline calculates overall trajectory success based on mode classification results of individual states, we can learn classifiers that achieve grounding as a by-product of predicting the overall task execution.


Learned Mode Partitions of Configuration Space for 2D Navigation Tasks

The navigation domain serves as a 2D abstraction of the modal structure for multi-step manipulation tasks, where each polygon represents a different mode with its boundary showing the constraint. The goal is to traverse through the sequence consecutively as demonstrated until reaching the last colored polygon (e.g. mode 1 -> 2 -> 3 -> 4 -> 5). Mode transitions violating the demonstrated temporal ordering will lead to task failure (e.g. mode 1 -> 3/4/5).

(a) Demonstrations and ground truth modes

(b) Grounding learned by GLiDE

(c) GLiDE without counterfactual data

(d) GLiDE with incorrect number of modes

(e) Baseline: similarity-based clustering

Learned Mode Partitions Explain Why Some Perturbed Trajectories Succeed While Others Fail

Learned classifier explains success trajectories by checking if all transitions incur 0 cost (shown by the whiteness of dots). It also explains failure trajectories by checking if there exists at least one invalid transition (indicated by the blackness of dots).

Successful Execution 1

Successful Execution 2

Failing Counterfactual 1

Failing Counterfactual 2

Learned Mode Partitions Allow Planning Reactive Behavior for 2D Navigation Tasks

The learned grounding classifier can be used to segment whole demonstrations into sub-sequences for each mode, for which we extract the sub-goal (the average crossing point at each boundary) for each mode. Additionally, the explicit mode partitions allow us construct a potential flow controller for each mode that directs the system to reach each sub-goal without leaving the current mode prematurely. Should the execution be derailed by external perturbations, planning without and with the learned mode boundaries can lead to execution failures (left) or successes (right), respectively.

Planning without learned grounding leads to failures due to invalid mode transitions

Planning with learned grounding leads to successful recovery that obeys mode constraints

Self-Supervised Data Collection for 2D Navigation Tasks

To generate the data for training the grounding classifier, we first collect a few successful demonstrations from humans. We then apply synthetic perturbations to demonstration replays to generate additional successful trajectories and failing counterfactuals. Assuming an automatic reset mechanism and a labeling function that decides the trajectory execution outcome, we can collect a large dataset without humans in the loop.

Data Collection Setup

Human demonstrations: Mode 1 -> 2 -> 3 -> 4 -> 5. Invalid mode transitions: 1 -> 3 or 1 -> 4 or 1 -> 5

Add synthetic perturbations to demonstration replays with auto-labeling and auto-reset

Learning Image-based Grounding Classifier 2D Navigation Tasks

Given a dataset of perturbed replays with only sparse labels, we learn a classifier that maps image observations to the their corresponding modes shown by distinct colors.

Learned Grounding Classifier Enables Replanning for 2D Navigation Tasks

Once we learn the classifier, we can use it to generate a mode-informed policy that replans when a perturbation would lead the original policy to incur an invalid mode transition (e.g., white directly to blue/yellow/red)

Naive policy: without knowledge of the modes, the robot simply continues after perturbation which can lead to invalid mode transitions.

Mode-conditioned policy: By accurately recognizing the underlying modes, our method allows for recovery when perturbations cause unexpected mode transitions.

Learned Mode Segmentation of Demonstrations for Robosuite Manipulation Tasks

(Left) We manually set a ground truth based on RoboSuite state information to benchmark our learned mode segmentation, visualized in the left figure. Can Task: pick up the can and place it in the bottom-right bin. Lifting Task: pick up the block above a specified height. Square Peg Task: pick up the square nut and place it on the square peg. You can view pictures of all of the classification results here. (Below) To show the learned grounding can facilitate planning using LLMs, we apply perturbations to Behavioral Cloning (BC) rollouts below. BC performance drops off significantly with perturbations, but our method generally recovers better after the robot drops the can as the robot will be directed to pick up the can again.

Learned Grounding facilitates replanning with LLMs to improve execution success rate for RoboSuite Tasks

Behavior cloning (BC) rollouts: 93% success
BC rollouts (perturbed): 20% success
Mode-conditioned BC (perturbed): 40% success

Learned Grounding Classifier Enables Replanning for Real-World Scooping Task

A scooping task that requires the robot to transport at least a marble from one bowl to the other. Given external perturbations, the robot may drop marbles during transport, leading to a failed execution. When an imitation policy does not pay attention to the classified modes (shown on the right in the video), it cannot recover from such failures. In constrast, a mode-conditioned policy can replan and recover from such perturbations.

Mode-agnostic BC cannot recover from task-level perturbations (e.g. dropping all marbles)

Mode-conditioned BC enabled by the learned grounding can leverage LLM to replan

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
Yanwei Wang, Nadia Figueroa, Shen Li, Ankit Shah, Julie Shah
arxiv / code / project page
CoRL 2022 (Oral, acceptance rate: 6.5%)
IROS 2023 Workshop ( Best Student Paper, Learning Meets Model-based Methods for Manipulation and Grasping Workshop)

We present a continuous motion imitation method that can provably satisfy any discrete plan specified by a Linear Temporal Logic (LTL) formula. Consequently, the imitator is robust to both task- and motion-level disturbances and guaranteed to achieve task success.