Welcome to the ALR-Lab                                       

We combine a mixture of movement primitives with a distribution matching objective to learn versatile behaviors that match the expert’s behavior and versatility.

We study how recent State Space modeling approaches for Model-Based RL represent uncertainties. We find some flaws and propose a theoretically better-grounded alternative. We show it improves performance in tasks where it is important to appropriately capture uncertainty. If you want to know who this relates to the cat and the hamster you have to read the paper.

Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a controller, often represented as a movement primitive, for a given task descriptor called a context.

We present a novel deep hierarchical variational autoencoder that can serve as a basis for many fusion tasks. It can generate diverse image samples that are conditioned on multiple noisy, occluded, or only partially visible input images. We created three novel image fusion datasets and show that our method outperforms traditional approaches significantly.

We present a novel deep learning method that estimates the 6D poses of all objects in a cluttered scene based on multiple RGB-D images. Our approach is considerably more accurate than previous approaches especially for very occluded objects and it is robust towards dynamic camera setups as well as inaccurate camera calibration.

We propose a new formulation for a residual hybrid inverse dynamics model, which combines a fully physically consistent rigid-body dynamics model with a recurrent LSTM and a Coulomb friction function. The model is trained end-to-end using a new formulation of Barycentric Parameters called “Differentiable Barycentric”, which implicitly guarantees all conditions of physical consistency. In our real robot motion tracking experiments we show, that the new model is able to achieve compliant and precise motion tracking on unseen movements.

In this paper, we present a new approach for interactive scene segmentation using deep reinforcement learning. Our robot can learn to push objects in a heap such that semantic segmentation algorithms can detect every object in the heavily cluttered heap. 

Mechanical Search (MS)  is a framework for object retrieval, which uses a heuristic algorithm for pushing and rule-based algorithms for high-level planning. While rule-based policies profit from human intuition in how they work, they usually perform sub-optimally in many cases. We present am deep hierarchical reinforcement learning (RL) algorithm to perform this task, showing an increased search performance in terms of number of needed manipulation, success rate as well as computation time!

We design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation, which are of unprecedented complexity in the meta-learning domain for computer vision with exhaustively evaluation of common meta-learning techniques to strengthen the generalization capability. Furthermore, we propose functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion.

We propose a multi-task deep Kalman model, that can adapt to changing dynamics and environments. The model gives state of the art performance on several robotic benchmarks with non-stationarity with little computational overhead!!!

We propose a new method which enables robots to learn versatile and highly accurate skills in the contextual policy search setting by optimizing a mixture of experts model. We make use of Curriculum Learning, where the agent concentrates on local context regions it favors. Mathematical properties allow the algorithm to adjust the model complexity to find as many solutions as possible.

A video presenting our work can be found here.

We developped a new residual reinforcement learning method that not just manipulated the output of a controller but also its input (e.g., the set-points). We applied this method to a real robot peg-in-the hole setup with a significant amount of position and orientation uncertainty.

Video

Do you like Deep RL methods such as TRPO or PPO? Then you will also like this one! Our differentiable trust region layers can be used on top of any policy optimization algorithms such as policy gradients to obtain stable updates -- no approximations or implementation choices required :) Performance is enpar with PPO on simple exploration scenarios while we outperform PPO on more complex exploration environments.

Neural Processes are powerful tools for probabilistic meta-learning. Yet, they use rather basic aggregation methods, i.e. using a mean aggregator for the context, which does not give consistent uncertainty estimates and leads to poor prediction performance. Aggregating in a Bayesian way using Gaussian conditioning does a much better job !:)

Action conditional probabilistic model inspired by Kalman filter operations in the latent state. Find out how we learn the complex non-markovian dynamics of pneumatic soft robots and large hydraulic robots with this disentangled state and action representation.

Many methods for machine learning rely on approximate inference from intractable probability distributions. Learning sufficiently accurate approximations requires a rich model family and careful exploration of the relevant modes of the target distribution...

 Using the I-Projection for Mixture Density Estimation. Find out why maximum likelihood is not well suited for mixture density modelling and why you should use the I-projection instead.  

The Autonomous Learning Robots (ALR) Lab was founded at Jan. 2020 at the KIT. The new group is now building up and looking forward to do exciting research and teaching!