6-DOF Grasping for Target-driven Object Manipulation in Clutter

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is insufficient for many real-world scenarios and greatly limits possible grasps. We present a method that plans 6-DOF grasps for any desired object in a cluttered scene from partial point cloud observations. Our method achieves a grasp success of 80.3%, outperforming baseline approaches by 17.6% and clearing 9 cluttered table scenes (which contain 23 unknown objects and 51 picks in total) on a real robotic platform. By using our learned collision checking module, we can even reason about effective grasp sequences to retrieve objects that are not immediately accessible. Supplementary video can be found at https://youtu.be/w0B5S-gCsJk.

Camera-to-Robot Pose Estimation from a Single Image

We present an approach for estimating the pose of a camera with respect to a robot from a single image. Our method uses a deep neural network to process an RGB image from the camera to detect 2D keypoints on the robot. The network is trained entirely on simulated data using domain randomization. Perspective-$n$-point (P$n$P) is then used to recover the camera extrinsics, assuming that the joint configuration of the robot manipulator is known. Unlike classic hand-eye calibration systems, our method does not require an off-line calibration step but rather is capable of computing the camera extrinsics from a single frame, thus opening the possibility of on-line calibration. We show experimental results for three different camera sensors, demonstrating that our approach is able to achieve accuracy with a single frame that is better than that of classic off-line hand-eye calibration using multiple frames. With additional frames, accuracy improves even further. Code, datasets, and pretrained models for three widely-used robot manipulators will be made available.

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Long composition rollouts with non-reversible state changes are among the phenomena we include to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like "Rinse off a mug and place it in the coffee maker." and low-level language instructions like "Walk to the coffee maker on the right." ALFRED tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets. We show that a baseline model designed for recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark.

LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation

Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a result, they are difficult to scale to a large number of objects and cannot be directly applied to unseen objects. In this work, we propose a novel framework for 6D pose estimation of unseen objects. We design an end-to-end neural network that reconstructs a latent 3D representation of an object using a small number of reference views of the object. Using the learned 3D representation, the network is able to render the object from arbitrary views. Using this neural renderer, we directly optimize for pose given an input image. By training our network with a large number of 3D shapes for reconstruction and rendering, our network generalizes well to unseen objects. We present a new dataset for unseen object pose estimation--MOPED. We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet dataset.

Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies

Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenges of using simulation, to date, has been to bridge the reality gap, so that policies trained in simulation can be deployed in the real world. We explore the reality gap in the context of learning a contextual policy for multi-fingered robotic grasping. We propose a Grasping Objects Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap problem. In our approach we use human hand motion demonstration to initialize and reduce the search space for learning. We contextualize our policy with the bounding cuboid dimensions of the object of interest, which allows the policy to work on a more flexible representation than directly using an image or point cloud. Leveraging fingertip touch sensors in the hand allows the policy to overcome the reduction in geometric information introduced by the coarse bounding box, as well as pose estimation uncertainty. We show our learned policy successfully runs on a real robot without any fine tuning, thus bridging the reality gap.

Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection

In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipulator arm to avoid obstacles in the environment and plan an end-effector pose for grasping. While trajectory planning and grasp planning are often tackled separately, how to efficiently integrate the two planning problems remains a challenge. In this work, we present a novel method for joint motion and grasp planning. Our method integrates manipulation trajectory optimization with online grasp synthesis and selection, where we apply online learning techniques to select goal configurations for grasping, and introduce a new grasp synthesis algorithm to generate grasps online. We evaluate our planning approach and demonstrate that our method generates robust and efficient motion plans for grasping in cluttered scenes.

Motion Reasoning for Goal-Based Imitation Learning

We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and reproduce the same goal in a completely different environment. The key challenge is that the goal of a video demonstration is often ambiguous at the level of semantic actions. The human demonstrators might unintentionally achieve certain subgoals in the demonstrations with their actions. Our main contribution is to propose a motion reasoning framework that combines task and motion planning to disambiguate the true intention of the demonstrator in the video demonstration. This allows us to robustly recognize the goals that cannot be disambiguated by previous action-based approaches. We evaluate our approach by collecting a dataset of 96 video demonstrations in a mockup kitchen environment. We show that our motion reasoning plays an important role in recognizing the actual goal of the demonstrator and improves the success rate by over 20%. We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment.

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task. However, leveraging a fixed batch of data can be problematic for larger datasets and longer-horizon tasks with greater variations. The data can exhibit substantial diversity and consist of suboptimal solution approaches. In this paper, we propose Implicit Reinforcement without Interaction at Scale (IRIS), a novel framework for learning from large-scale demonstration datasets. IRIS factorizes the control problem into a goal-conditioned low-level controller that imitates short demonstration sequences and a high-level goal selection mechanism that sets goals for the low-level and selectively combines parts of suboptimal solutions leading to more successful task completions. We evaluate IRIS across three datasets, including the RoboTurk Cans dataset collected by humans via crowdsourcing, and show that performant policies can be learned from purely offline learning. Additional results and videos at https://stanfordvl.github.io/iris/ .

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require opening a drawer to observe its contents or moving an object out of the way to examine the space behind it. If the robot fails to detect an important object, it must update its belief about the world and compute a new plan of action. Additionally, a robot that acts noisily will never exactly arrive at a desired state. Still, it is important that the robot adjusts accordingly in order to keep making progress towards achieving the goal. In this work, we present an online planning and execution system for robots faced with these kinds of challenges. Our approach is able to efficiently solve partially observable problems both in simulation and in a real-world kitchen.