Hybrid control trajectory optimization under uncertainty

Pajarinen, Joni; Kyrki, Ville; Koval, Michael C.; Srinivasa, Siddhartha S.; Peters, Jan; Neumann, Gerhard

doi:10.1109/iros.2017.8206460

Cited by 12 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid Planning in Manipulation: Hybrid Differential Dynamic Programming [13], [14] aims to solve a hybrid optimal control problem combining discrete actions and continuous control inputs. The work in [13] optimized continuous mixtures of discrete actions.…”

Section: Related Workmentioning

confidence: 99%

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

et al. 2021

View full text Add to dashboard Cite

This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequential manipulation for long-horizon and versatile tasks in dynamic environments. At the symbolic planning level, we propose a scalable decision-making method for long-horizon manipulation tasks using the Planning Domain Definition Language (PDDL) with causal graph decomposition. At the motion planning level, we devise a trajectory optimization (TO) approach based on the Differential Dynamic Programming (DDP) and Alternating Direction Method of Multipliers (ADMM), suitable for solving constrained, large-scale nonlinear optimization in a distributed manner. Distinct from conventional geometric motion planners, our approach generates highly dynamic manipulation motions by incorporating the full robot and object dynamics. Furthermore, in lieu of a hierarchical planning approach, we solve a holistically integrated bilevel optimization problem involving costs from both the low-level TO and the high-level search. Simulation and experimental results demonstrate dynamic manipulation for long-horizon object sorting tasks in clutter and on a moving conveyor belt.

show abstract

Section: Related Workmentioning

confidence: 99%

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Hogan et al (2017) used MPC to find an optimal sequence of robot motions to achieve the desired object motion. Pajarinen et al (2017) solved the problem of finding an optimal sequence of hybrid controls under uncertainty using differential dynamic programming (DDP) and incorporating discrete actions inside DDP.…”

Section: Related Workmentioning

confidence: 99%

“…We learn simplified dynamics models and incorporate them into the online manipulation planning where there is no prior knowledge of action sequences as used in the state-of-the-art literature (Hogan et al, 2017; Pajarinen et al, 2017). The reason that we use a learning method to fit the models is because pure analytic models of complex dynamics are difficult to obtain for accurate planning.…”

Section: Introductionmentioning

confidence: 99%

A model predictive approach for online mobile manipulation of non-holonomic objects using learned dynamics

Novin

Yazdani

Hermans

2021

The International Journal of Robotics Research

View full text Add to dashboard Cite

Assistive robots designed for physical interaction with objects will play an important role in assisting with mobility and fall prevention in healthcare facilities. Autonomous mobile manipulation presents a hurdle prior to safely using robots in real-life applications. In this article, we introduce a mobile manipulation framework based on model predictive control using learned dynamics models of objects. We focus on the specific problem of manipulating legged objects such as those commonly found in healthcare environments and personal dwellings (e.g., walkers, tables, chairs). We describe a probabilistic method for autonomous learning of an approximate dynamics model for these objects. In this method, we learn dynamic parameters using a small dataset consisting of force and motion data from interactions between the robot and object. Moreover, we account for multiple manipulation strategies by formulating manipulation planning as a mixed-integer convex optimization. The proposed framework considers the hybrid control system composed of (i) choosing which leg to grasp and (ii) control of continuous applied forces for manipulation. We formalize our algorithm based on model predictive control to compensate for modeling errors and find an optimal path to manipulate the object from one configuration to another. We present results for several objects with various wheel configurations. Simulation and physical experiments show that the obtained dynamics models are sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot successfully moves the object to the desired pose while avoiding any collision.

show abstract

“…The spline representation is a smooth description of the object's surface from which all relevant properties along with their gradients can be extracted, like normal and tangent vectors. The use of continuous representations of the object's surface is more generic than approaches like [69] that rely on the convexification of the object's shape, as scaling with respect to the number of edges/phases becomes cumbersome.…”

Section: ) Object's Shape Representationmentioning

confidence: 99%

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

et al. 2020

View full text Add to dashboard Cite

Effective collaboration is based on online adaptation of one's own actions to the actions of their partner. This article provides a principled formalism to address online adaptation in joint planning problems such as Dyadic collaborative Manipulation (DcM) scenarios. We propose an efficient bilevel formulation that combines graph search methods with trajectory optimization, enabling robotic agents to adapt their policy on-the-fly in accordance to changes of the dyadic task. This method is the first to empower agents with the ability to plan online in hybrid spaces; optimizing over discrete contact locations, contact sequence patterns, continuous trajectories, and force profiles for co-manipulation tasks. This is particularly important in large object co-manipulation that requires changes of grasp-holds and plan adaptation. We demonstrate in simulation and with robot experiments the efficacy of the bilevel optimization by investigating the effect of robot policy changes in response to real-time alterations of the dyadic goals, eminent grasp switches, as well as optimal dyadic interactions to realize the joint task. Index Terms-Dual arm manipulation (DaM), manipulation planning, optimization and optimal control, physical human-robot interaction. I. INTRODUCTION D YADIC collaborative Manipulation (DcM) is a term we use to refer to a set of two individuals jointly manipulating an object, as shown in Fig. 1. The two individuals partner together to form a distributed system, augmenting their Manuscript

show abstract

Hybrid control trajectory optimization under uncertainty

Cited by 12 publications

References 33 publications

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

A model predictive approach for online mobile manipulation of non-holonomic objects using learned dynamics

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

Contact Info

Product

Resources

About