Alan D. Christiansen

About

Alan D. Christiansen is a pioneering researcher whose work spans robotic learning, autonomous manipulation, and multiobjective optimization — fields that sit at the intersection of artificial intelligence and robotics engineering. Beginning in the late 1980s, Christiansen pursued a fundamental challenge in robotics: enabling machines to learn how their actions affect the physical world without relying on pre-programmed models. His landmark work on learning reliable manipulation strategies, which has accumulated nearly 40 citations across multiple publications, demonstrated that robots could acquire increasingly accurate action models through exploration and observation alone — a conceptually bold departure from conventional human-programmed approaches. His doctoral research on automatic acquisition of task theories formalized this framework, providing robots a mechanism to build predictive models from observed experience. On the engineering side, Christiansen made notable contributions to robot arm design optimization, developing hybrid genetic algorithm approaches combined with min-max multiobjective methods to identify Pareto-optimal design solutions — work that has garnered over 40 citations. His investigations into planning under uncertainty, particularly in stochastic environments modeled as Markov processes, further demonstrated his commitment to making robots reliable in real-world conditions. Collectively, his body of work helped lay important groundwork for modern autonomous and learning-capable robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Using a new GA-based multiobjective optimization technique for the design of robot arms

   41 citations · 1998
2. 2
   Learning reliable manipulation strategies without initial physical models

   39 citations · 2002
3. 3
   Automatic acquisition of task theories for robotic manipulation

   21 citations · 1992
4. 4
   Learning reliable manipulation strategies without initial physical models

   15 citations · 1991
5. 5
   Multiobjective design optimization of counterweight balancing of a robot arm using genetic algorithms

   15 citations · 1995
6. 6
   EXPERIMENTS IN ROBOT LEARNING

   13 citations · 1989
7. 7
   Learning reliable manipulation strategies without initial physical models

   7 citations · 1993
8. 8
   Learning to Predict in Uncertain Continuous Tasks

   6 citations · 1992
9. 9
   Comparing two algorithms for automatic planning by robots in stochastic environments

   5 citations · 1995
10. 10
    Toward a learning robot

    4 citations · 2018

Key Collaborators

Contact & Links