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Kris Hauser is a roboticist whose research concerns robot motion planning and control. The University of Illinois identifies him as a professor working across computing and engineering, with research on decision-making for physical robots under limits such as uncertain dynamics and bounded computation.^[ref]

His identified university profile provides an external portrait of Kris Hauser.

His author-hosted Robotic Systems by Kris Hauser brings geometry, motion selection and contact mechanics into one technical account. Its contact-planning treatment explains why selecting contact and selecting motion are coupled: making or breaking contact changes the constraints on the robot.^[ref] A collision-free trajectory alone does not establish that the available contacts can produce the required forces.

An original static planar example makes that distinction quantitative. Suppose two nonadhesive point contacts support a body of gravitational weight $W=200\mathrm{N}$ at horizontal positions $x_1=0$ and $x_2=0.5\mathrm{m}$. With center of mass coordinate $x_c$ and no other applied force or moment, vertical force and torque balance giveAt $x_c=0.20\mathrm{m}$, the normal reactions are $F_1=120\mathrm{N}$ and $F_2=80\mathrm{N}$. Moving the center to $x_c=0.60\mathrm{m}$ while retaining those same contacts would require $F_1=-40\mathrm{N}$ and $F_2=240\mathrm{N}$. A negative normal reaction violates the nonadhesive contact model. A planner must change the motion, contacts or assumed external forces; a path that avoids obstacles need not satisfy support constraints.

This is a derived example of the modeling question, not an experiment attributed to Hauser. His text supplies the explicit unilateral-contact and equilibrium framework.^[ref] A proposed robot test compares predicted reactions with calibrated contact measurements while shifting a controlled load, recording any change of contact rather than forcing the original static model onto a changed apparatus.

A roboticist develops or studies robots by combining their mechanical design, sensing, computation and control. The work connects a desired physical task to a machine that can actually perform it. Kinematics describes permitted motion; robot motion planning selects motions subject to constraints; feedback control uses measurements to correct execution. An algorithm and the apparatus it controls must therefore be evaluated together.

For an original planar robot arm example, let two links have lengths $l_1,l_2$ and relative joint angles ${\theta }_1,{\theta }_2$. Its end effector position isWith both lengths $0.5\mathrm{m}$ and angles $(0,\pi /2)$, the endpoint is $(0.5,0.5)\mathrm{m}$. A small first-joint offset $\delta {\theta }_1=0.01$ radians changes the position to first order by $(-y,x)\delta {\theta }_1=(-0.005,0.005)\mathrm{m}$, an error magnitude about $7.07\mathrm{mm}$. Accurate commanded numbers do not imply equally accurate physical geometry.

A proposed investigation compares measured endpoint positions with the kinematics model over several configurations, then checks whether the discrepancies follow a joint offset, a link-length error or changing mechanical deflection. Those alternatives suggest different corrections. It also tests execution on the real robot, rather than treating successful numerical planning as sufficient evidence of task completion.

Kris Hauser's author-hosted Robotic Systems by Kris Hauser connects geometric configurations, inverse kinematics, motion selection and contact constraints in one technical account.^[ref] Its contact discussion makes another key distinction: deciding when to touch the environment changes the applicable physical constraints. A roboticist may consequently work with mechanisms, algorithms or experiments while still needing a common model of what is sensed, commanded and physically achieved.