Abstract

In this paper, we present an algorithmic frame-work which computes the collision-free velocities for the robotin a human shared dynamic and uncertain environment. Weextend the concept of Inverse Velocity Obstacle (IVO) to aprobabilistic variant to handle the state estimation and motionuncertainties that arise due to the other participants of the en-vironment. These uncertainties are modeled as non-parametricprobability distributions. In our PIVO: Probabilistic InverseVelocity Obstacle, we propose the collision-free navigation asan optimization problem by reformulating the velocity condi-tions of IVO as chance constraints that takes the uncertaintyinto account. The space of collision-free velocities that resultfrom the presented optimization scheme are associated to aconfidence measure as a specified probability. We demonstratethe efficacy of our PIVO through numerical simulations anddemonstrating its ability to generate safe trajectories underhighly uncertain environments

Abstract

In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (eg. image sequences). But, such methods do not generalize to datasets they have not been trained on. We propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information, we rely on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see fig. above). We demonstrate that using semantics provides a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferrable representations and ensure reproducibility, we release all our code and data. 3

Abstract

In this paper, we present an algorithmic framework which computes the collision-free velocities for the robot in a human shared dynamic and uncertain environment. We extend the concept of Inverse Velocity Obstacle (IVO) to a probabilistic variant to handle the state estimation and motion uncertainties that arise due to the other participants of the environment. These uncertainties are modeled as non-parametric probability distributions. In our PIVO: Probabilistic Inverse Velocity Obstacle, we propose the collision-free navigation as an optimization problem by reformulating the velocity conditions of IVO as chance constraints that takes the uncertainty into account. The space of collision-free velocities that result from the presented optimization scheme are associated to a confidence measure as a specified probability. We demonstrate the efficacy of our PIVO through numerical simulations and demonstrating its ability to generate safe trajectories under highly uncertain environments.

Abstract

Abstract— We present here, a novel network architecture called MergeNet for discovering small obstacles for on-road scenes in the context of autonomous driving. The basis of the architecture rests on the central consideration of training with less amount of data since the physical setup and the annotation process for small obstacles is hard to scale. For making effective use of the limited data, we propose a multi-stage training procedure involving weight-sharing, separate learning of low and high level features from the RGBD input and a refining stage which learns to fuse the obtained complementary features. The model is trained and evaluated on the Lost and Found dataset and is able to achieve state-of-art results with just 135 images in comparison to the 1000 images used by the previous benchmark. Additionally, we also compare our results with recent methods trained on 6000 images and show that our method achieves comparable performance with only 1000 training samples.

Abstract

This paper strives to answer the following question: Is it possible to recognize an intersection when seen from different road segments that constitute the intersection? An intersection or a junction typically is a meeting point of three or four road segments. Its recognition from a road segment that is transverse to or 180 degrees apart from its previous sighting is an extremely challenging and yet a very relevant problem to be addressed from the point of view of both autonomous driving as well as loop detection. This paper formulates this as a problem of video recognition and proposes a novel LSTM based Siamese style deep network for video recognition. For what is indeed a challenging problem and the limited annotated dataset available we show competitive results of recognizing intersections when approached from diverse viewpoints or road segments. Specifically, we tabulate effective recognition accuracy even …

Abstract

Performing dual arm coordinated (reachability)tasks in humanoid robots require complex planning strategies and this complexity increases further, in case of humanoids with articulated torso. These complex strategies may not be suitable for online motion planning. This paper proposes a faster way to accomplish dual arm coordinated tasks using methodology based on Reinforcement Learning. The contribution of this paper is twofold. Firstly, we propose DiGrad (Differential Gradients), a new RL framework for multi-task learning in manipulators. Secondly, we show how this framework can be adopted to learn dual arm coordination in a 27 degrees of freedom (DOF)humanoid robot with articulated spine. The proposed framework and methodology are evaluated in various environments and simulation results are presented. A comparative study of DiGrad with its parent algorithm in different settings is also presented

Abstract

General Inverse Kinematic (IK) solvers may not guarantee real-time control of the end-effectors in external coordinates along with maintaining stability. This work addresses this problem by using Reinforcement Learning (RL) for learning an inverse kinematics solver for reachability tasks which ensures stability and self-collision avoidance while solving for end effectors. We propose an actor-critic based algorithm to learn joint space trajectories of stable configuration for solving inverse kinematics that can operate over continuous action spaces. Our approach is based on the idea of exploring the entire workspace and learning the best possible configurations. The proposed strategy was evaluated on the highly articulated upper body of a 27 degrees of freedom (DoF) humanoid for learning multi-goal reachability tasks of both hands along with maintaining stability in double support phase. We show that the trained model was able to solve inverse kinematics for both the hands, where the articulated torso contributed to both the tasks.

Abstract

With the advent of artificial intelligence and machine learning, humanoid robots are made to learn a variety of skills which humans possess. One of fundamental skills which humans use in day-to-day activities is performing tasks with coordination between both the hands. In case of humanoids, learning such skills require optimal motion planning which includes avoiding collisions with the surroundings. In this paper, we propose a framework to learn coordinated tasks in cluttered environments based on DiGrad - A multi-task reinforcement learning algorithm for continuous action-spaces. Further, we propose an algorithm to smooth the joint space trajectories obtained by the proposed framework in order to reduce the noise instilled during training. The proposed framework was tested on a 27 degrees of freedom (DoF) humanoid with articulated torso for performing coordinated object-reaching task with both the hands in four different environments with varying levels of difficulty. It is observed that the humanoid is able to plan collision free trajectory in real-time. Simulation results also reveal the usefulness of the articulated torso for performing tasks which require coordination between both the arms.

Abstract

Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a compound policy may be learnt with shared neural network parameters, which performs multiple tasks concurrently. However such compound policy may get biased towards a task or the gradients from different tasks negate each other, making the learning unstable and sometimes less data efficient. In this paper, we propose a new approach for simultaneous training of multiple tasks sharing a set of common actions in continuous action spaces, which we call as DiGrad (Differential Policy Gradient). The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network. We also propose a simple heuristic in the differential policy gradient update to further improve the learning. The proposed architecture was tested on 8 link planar manipulator and 27 degrees of freedom (DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2 end effectors respectively. We show that our approach supports efficient multi-task learning in complex robotic systems, outperforming related methods in continuous action spaces.

Abstract

We propose a novel Line based parameterization for category specific CAD models. The proposed parameterization associates 3D category-specific CAD model and object under consideration using a dictionary based RANSAC method that uses object Viewpoints as prior and edges detected in the respective intensity image of the scene. The association problem is posed as a classical Geometry problem rather than being dataset driven, thus saving the time and labour that one invests in annotating dataset to train Keypoint Network[1, 2] for different category objects. Besides eliminating the need of dataset preparation, the approach also speeds up the entire process as this method processes the image only once for all objects, thus eliminating the need of invoking the network for every object in an image across all images. A 3D-2D edge association module followed by a resection algorithm for lines is used to recover object poses. The formulation optimizes for shape and pose of the object, thus aiding in recovering object 3D structure more accurately. Finally, a Factor Graph formulation is used to combine object poses with camera odometry to formulate a SLAM problem.