Scene-level Programming by Demonstration (PbD) is faced with an important challenge - perceptual uncertainty. Addressing this problem, we present a scene-level PbD paradigm that programs robots to perform goal-directed manipulation in unstructured environments with grounded perception. Scene estimation is enabled by our discriminatively-informed generative scene estimation method (DIGEST). Given scene observations, DIGEST utilizes candidates from discriminative object detectors to generate and evaluate hypothesized scenes of object poses. Scene graphs are generated from the estimated object poses, which in turn is used in the PbD system for high-level task planning. We demonstrate that DIGEST performs better than existing method and is robust to false positive detections. Building a PbD system on DIGEST, we show experiments of programming a Fetch robot to set up a tray for delivery with various objects through demonstration of goal scenes.
more »
« less
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot
then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user’s goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user’s demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST)
method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.
more »
« less
- Award ID(s):
- 1638047
- NSF-PAR ID:
- 10066632
- Date Published:
- Journal Name:
- IEEE/RSJ International Conference on Robotics and Automation (ICRA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Performing robust goal-directed manipulation tasks remains a crucial challenge for autonomous robots. In an ideal case, shared autonomous control of manipulators would allow human users to specify their intent as a goal state and have the robot reason over the actions and motions to achieve this goal. However, realizing this goal remains elusive due to the problem of perceiving the robot’s environment. We address and describe the problem of axiomatic scene estimation for robot manipulation in cluttered scenes which is the estimation of a tree-structured scene graph describing the configuration of objects observed from robot sensing. We propose generative approaches to scene inference (as the axiomatic particle filter, and the axiomatic scene estimation by Markov chain Monte Carlo based sampler) of the robot’s environment as a scene graph. The result from AxScEs estimation are axioms amenable to goal-directed manipulation through symbolic inference for task planning and collision-free motion planning and execution. We demonstrate the results for goal-directed manipulation of multi-object scenes by a PR2 robot.more » « less
-
Human-robot collaboration systems benefit from recognizing people’s intentions. This capability is especially useful for collaborative manipulation applications, in which users operate robot arms to manipulate objects. For collaborative manipulation, systems can determine users’ intentions by tracking eye gaze and identifying gaze fixations on particular objects in the scene (i.e., semantic gaze labeling). Translating 2D fixation locations (from eye trackers) into 3D fixation locations (in the real world) is a technical challenge. One approach is to assign each fixation to the object closest to it. However, calibration drift, head motion, and the extra dimension required for real-world interactions make this position matching approach inaccurate. In this work, we introduce velocity features that compare the relative motion between subsequent gaze fixations and a nite set of known points and assign fixation position to one of those known points. We validate our approach on synthetic data to demonstrate that classifying using velocity features is more robust than a position matching approach. In addition, we show that a classifier using velocity features improves semantic labeling on a real-world dataset of human-robot assistive manipulation interactions.more » « less
-
Human-robot collaboration systems benefit from recognizing people’s intentions. This capability is especially useful for collaborative manipulation applications, in which users operate robot arms to manipulate objects. For collaborative manipulation, systems can determine users’ intentions by tracking eye gaze and identifying gaze fixations on particular objects in the scene (i.e., semantic gaze labeling). Translating 2D fixation locations (from eye trackers) into 3D fixation locations (in the real world) is a technical challenge. One approach is to assign each fixation to the object closest to it. However, calibration drift, head motion, and the extra dimension required for real-world interactions make this position matching approach inaccurate. In this work, we introduce velocity features that compare the relative motion between subsequent gaze fixations and a finite set of known points and assign fixation position to one of those known points. We validate our approach on synthetic data to demonstrate that classifying using velocity features is more robust than a position matching approach. In addition, we show that a classifier using velocity features improves semantic labeling on a real-world dataset of human-robot assistive manipulation interactions.more » « less
-
Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and the results demonstrate the effectiveness and superiority of our model.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
